Method of obtaining high accuracy urination information

ABSTRACT

A method of obtaining high accuracy urination information is proposed. There may be provided the method of obtaining the urination information, wherein sound data is divided into a plurality of windows, segmented target data corresponding to respective windows is obtained from the sound data, segmented classification data classifying urination sections or non-urination sections and segmented urine flow rate data are obtained by using the obtained segmented target data, and urination data is obtained by using the obtained segmented classification data and the segmented urine flow rate data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of PCT International Application No.PCT/KR2022/002956, filed Mar. 2, 2022, which claims priority to KoreanPatent Application No. 10-2021-0027771, filed Mar. 2, 2021, the entirecontents of which are hereby expressly incorporated by reference.

TECHNICAL FIELD

The present specification relates to a method of obtaining high accuracyurination information and, more particularly, to a method of extractingurination information from urination-related sound data recorded byusing a urination/non-urination section classification model and a urineflow rate prediction model.

BACKGROUND ART

Sound generated from a person's body has been used as importantinformation in confirming a person's health status or whether a personhas a disease. In particular, the sound generated in a urination processof the person may include important information for diagnosing aperson's urination function, so study on a method of obtaining urinationinformation by analyzing urination sound is continuously beingconducted. Specifically, a urine flow rate or a urine volume of theperson may be predicted by analyzing sound data obtained by recordingsound in the urination process of the person.

Meanwhile, the sound generated in the urination process of the personmay include various sounds generated due to a surrounding environment inaddition to the urination-related sound. The sound caused by thesurrounding environment may be included in a urination section or anon-urination section in the urination process. In particular, in a casewhere the sound caused by the surrounding environment is included in thenon-urination section and reflected in the sound data, there is aproblem in that an analysis result is not accurate.

Therefore, in order to obtain more accurate urination information, amethod of analyzing the sound data for the urination process is requiredin consideration of the case where the sound caused by the surroundingenvironment is included in the non-urination section.

DISCLOSURE Technical Problem

An objective of the present specification to solve one problem is toprovide a method of obtaining urination information, wherein highaccuracy urination information is obtained from sound data obtained byrecording sound in the urination process of a person.

Another objective of the present specification to solve one problem isto provide a method of obtaining urination information by using a modelfor classifying the urination process into urination sections andnon-urination sections and a model for predicting a urine flow rate inthe urination process.

Yet another objective of the present specification to solve one problemis to provide a method of first determining whether or not a urinationprocess is occurring for collected sound data and then performing urineflow rate prediction.

The problem to be solved in this specification is not limited to theabove-mentioned problems, and the problems not mentioned will be clearlyunderstood by those skilled in the art to which the present disclosurebelongs from the present specification and accompanying drawings.

Technical Solution

According to one embodiment of the present specification, a method ofobtaining high accuracy urination information may be provided, themethod of obtaining urination information comprises: obtaining sounddata, wherein the sound data have a starting point and an ending point;obtaining first to m-th segmented target data corresponding to m windowsfrom the sound data, wherein each of the m windows has a predeterminedtime period, and is sequentially determined between the starting pointand the ending point, consecutive windows among the m windows partiallyoverlap each other, and the m is a natural number greater than or equalto 2; obtaining first to m-th segmented classification data by inputtingthe first to m-th segmented target data into a pre-trainedurination/non-urination classification model, wherein theurination/non-urination classification model is trained to output datacomprising at least one value for classifying a urination section or anon-urination section when first data matrix inputted; obtaining firstto n-th segmented target data corresponding to n windows from the sounddata, wherein each of the n windows has a predetermined time period, andis sequentially determined between the starting point and the endingpoint, consecutive windows among the n windows partially overlap eachother, and the n is the natural number greater than or equal to 2;obtaining first to n-th segmented urine flow rate data by inputting thefirst to n-th segmented target data into a pre-trained urine flow rateprediction model, wherein the urine flow rate prediction model istrained to output data comprising at least one value for urine flow ratewhen second data matrix inputted; and obtaining urination data using atleast the first to m-th segmented classification data and the first ton-th segmented urine flow rate data.

According to another embodiment of the present specification, a methodof obtaining high accuracy urination information may be provided, themethod of obtaining urination information comprises: obtaining sounddata by sampling a sound signal obtained by recording a urinationprocess through an external device, wherein the sound data have astarting point and ending point; obtaining first to n-th segmentedtarget data corresponding to first to n-th windows from the sound data,wherein each of the first to n-th windows has a predetermined timeperiod, and is sequentially determined between the starting point andthe ending point, and the n is a natural number greater than or equal to2; obtaining first to m-th segmented classification data by inputting atleast a part of the first to n-th segmented target data into apre-trained urination/non-urination classification model, wherein theurination/non-urination classification model is trained to output datacomprising at least one value for classifying a urination section or anon-urination section when first data matrix inputted, and the m is thenatural number greater than or equal to 2 and less than or equal to then; obtaining first to n-th segmented urine flow rate data by inputtingthe first to n-th segmented target data into a pre-trained urine flowrate prediction model, wherein the urine flow rate prediction model istrained to output data including at least one value for urine flow ratewhen second data matrix inputted; and obtaining urination data using atleast the first to m-th segmented classification data and the first ton-th segmented urine flow rate data.

The problem solutions of the present specification are not limited tothe above-described problem solutions, and solutions that are notmentioned may be understood clearly to those skilled in the art to whichthe present disclosure belongs from the present specification and theaccompanying drawings.

Advantageous Effects

According to an embodiment of the present specification, a result ofclassifying the urination process into the urination sections and thenon-urination sections and a result of predicting the urine flow rate inthe urination process are used together, whereby a prediction result ofa highly accurate urine flow rate for the urination process may beobtained.

According to an embodiment of the present specification, data obtainedby classifying the urination process into the urination sections and thenon-urination sections is used, whereby urine flow rate data in thenon-urination section is prevented from being included in a result ofpredicting the urine flow rate of the urination process.

According to an embodiment of the present specification, whether or nota urination process is occurring is by first determined prior topredicting a urinary flow rate, whereby unnecessary data analysis may beprevented in advance.

The effects according to the present specification are not limited tothe above-described effects, and effects not mentioned herein may beclearly understood by those skilled in the art to which the presentdisclosure belongs from the present specification and accompanyingdrawings.

DESCRIPTION OF DRAWINGS

FIG. 1 is a view illustrating an environment for analyzing urinationinformation according to an exemplary embodiment of the presentspecification.

FIG. 2 is a view illustrating a configuration of a sound analysis systemaccording to the exemplary embodiment of the present specification.

FIG. 3 is a view illustrating a process by which the configuration ofthe sound analysis system works according to the exemplary embodiment ofthe present specification.

FIGS. 4 and 5 are views illustrating a method of dividing sound dataaccording to windows according to the exemplary embodiment of thepresent specification.

FIG. 6 is a view illustrating a process of extracting feature valuesfrom the sound data according to the exemplary embodiment of the presentspecification.

FIGS. 7 and 8 are views illustrating a process of obtaining target datato be analyzed according to the exemplary embodiment of the presentspecification.

FIG. 9 is a view illustrating a process of obtaining urine flow ratedata by using a urine flow rate prediction model according to theexemplary embodiment of the present specification.

FIG. 10 is a view illustrating a method of obtaining candidate urineflow rate data according to the exemplary embodiment of the presentspecification.

FIG. 11 is a view illustrating a process of obtaining classificationdata by using a urination/non-urination classification model accordingto the exemplary embodiment of the present specification.

FIG. 12 is a view illustrating a method of obtaining urinationclassification data according to the exemplary embodiment of the presentspecification.

FIG. 13 is a view illustrating a method of obtaining urinationclassification data according to another exemplary embodiment of thepresent specification.

FIGS. 14 and 15 are views illustrating a method of obtaining urinationdata by using the candidate urine flow rate data and the urinationclassification data according to the exemplary embodiment of the presentspecification.

FIGS. 16 and 17 are flowcharts illustrating a method of analyzing theurination information according to the exemplary embodiment of thepresent specification.

FIG. 18 is a view illustrating a graph for comparing a result of a casewhen the urination/non-urination classification model is not used and aresult of a case when the urination/non-urination classification modelis used according to the exemplary embodiment of the presentspecification.

FIG. 19 is a flowchart illustrating a process of training the urine flowrate prediction model according to the exemplary embodiment of thepresent specification.

FIG. 20 is a flowchart illustrating a process of training theurination/non-urination classification model according to the exemplaryembodiment of the present specification.

FIG. 21 is a view illustrating actual measurement data and predictiondata according to the exemplary embodiment of the present specification.

FIG. 22 is a view illustrating the actual measurement data, theprediction data, and corrected prediction data according to theexemplary embodiment of the present specification.

FIG. 23 is a flowchart illustrating a method of correcting dataaccording to the exemplary embodiment of the present specification.

FIG. 24 is a view illustrating a process of correcting the dataaccording to the exemplary embodiment of the present specification.

FIG. 25 is a graph illustrating a relationship between the actualmeasurement data and the prediction data before data correctionaccording to the exemplary embodiment of the present specification.

FIG. 26 is a graph illustrating a relationship between the actualmeasurement data and the prediction data after the data correctionaccording to the exemplary embodiment of the present specification.

According to an embodiment of the present specification, a method ofobtaining high accuracy urination information may be provided, and themethod of obtaining urination information may include obtaining sounddata, wherein the sound data have a starting point and an ending point;obtaining first to m-th segmented target data corresponding to m windowsfrom the sound data, wherein each of the m windows has a predeterminedtime period, and is sequentially determined between the starting pointand the ending point, consecutive windows among the m windows partiallyoverlap each other, and the m is a natural number greater than or equalto 2; obtaining first to m-th segmented classification data by inputtingthe first to m-th segmented target data into a pre-trainedurination/non-urination classification model, wherein theurination/non-urination classification model is trained to output datacomprising at least one value for classifying a urination section or anon-urination section when first data matrix inputted; obtaining firstto n-th segmented target data corresponding to n windows from the sounddata, wherein each of the n windows has a predetermined time period, andis sequentially determined between the starting point and the endingpoint, consecutive windows among the n windows partially overlap eachother, and the n is the natural number greater than or equal to 2;obtaining first to n-th segmented urine flow rate data by inputting thefirst to n-th segmented target data into a pre-trained urine flow rateprediction model, wherein the urine flow rate prediction model istrained to output data comprising at least one value for urine flow ratewhen second data matrix inputted; and obtaining urination data using atleast the first to m-th segmented classification data and the first ton-th segmented urine flow rate data.

An overlapping degree of the consecutive windows among the m windows maybe different from an overlapping degree of consecutive windows amongthen windows.

The overlapping degree of the consecutive windows among the m windowsmay be less than the overlapping degree of consecutive windows amongthen windows.

Each of the first to m-th pieces segmented target data and the first ton-th segmented target data may include a feature value extracted from atleast a part of the sound data.

The obtaining the first to m-th segmented target data may includetransforming the sound data to spectrogram data; and obtaining the firstto m-th segmented target data corresponding to the first to m-th windowsfrom the spectrogram data.

The obtaining the first to m-th segmented target data may includeobtaining first to m-th segmented sound data corresponding to the firstto m-th windows; and transforming each of the first to m-th segmentedsound data to spectrogram data to obtain the first to m-th segmentedtarget data.

The spectrogram data may be obtained by applying a Mel-filter.

The first to m-th segmented classification data may be obtained bysequentially inputting the first to m-th segmented target data into theurination/non-urination classification model, and the first to n-thsegmented urine flow rate data may be obtained by sequentially inputtingthe first to n-th segmented target data into the urine flow rateprediction model.

The urination/non-urination classification model may include at leastfirst input layer, first convolution layer, first hidden layer, andfirst output layer, and the urine flow rate prediction model may includeat least second input layer, second convolution layer, second hiddenlayer, and second output layer.

A size of the first data matrix bay be equal to a size of the seconddata matrix.

The obtaining the urination data may include obtaining urinationclassification data using the first to m-th segmented classificationdata; obtaining candidate urine flow rate data using the first to n-thsegmented urine flow rate data; and processing the candidate urine flowrate data using the urination classification data.

The urination data may be obtained by convolution operating theurination classification data and the candidate urine flow rate data.

The method of obtaining urination information may further includedetermining whether a urination section exists for the sound data usingthe first to m-th segmented classification data after the obtaining thefirst to m-th segmented classification data, wherein when the urinationsection exists for the sound data, obtaining the first to n-th segmentedtarget data from the sound data, obtaining the first to n-th segmentedurine flow rate data by inputting the first to n-th segmented targetdata into the wine flow rate prediction model, and obtaining theurination data using at least the first to m-th segmented classificationdata and the first to n-th segmented urine flow rate data may beperformed.

According to another embodiment of the present specification, a methodof obtaining high accuracy urination information may be provided, andthe method of obtaining urination information may include obtainingsound data by sampling a sound signal obtained by recording a urinationprocess through an external device, wherein the sound data have astarting point and ending point; obtaining first to n-th segmentedtarget data corresponding to first to n-th windows from the sound data,wherein each of the first to n-th windows has a predetermined timeperiod, and is sequentially determined between the starting point andthe ending point, and then is a natural number greater than or equal to2; obtaining first to m-th segmented classification data by inputting atleast a part of the first to n-th segmented target data into apre-trained urination/non-urination classification model, wherein theurination/non-urination classification model is trained to output datacomprising at least one value for classifying a urination section or anon-urination section when first data matrix inputted, and the m is thenatural number greater than or equal to 2 and less than or equal to then; obtaining first to n-th segmented urine flow rate data by inputtingthe first to n-th segmented target data into a pre-trained urine flowrate prediction model, wherein the urine flow rate prediction model istrained to output data including at least one value for urine flow ratewhen second data matrix inputted; and obtaining urination data using atleast the first to m-th segmented classification data and the first ton-th segmented urine flow rate data.

The two consecutive windows among the first to n-th windows may overlap.

The two consecutive windows among the first to n-th windows may notoverlap each other, and them may be equal to then.

Each of the first to n-th segmented target data may include a featurevalue extracted from at least a part of the sound data.

The obtaining the first to n-th segmented target data may includetransforming the sound data to spectrogram data; and obtaining the firstto n-th segmented target data corresponding to the first to n-th windowsfrom the spectrogram data.

The obtaining the first to n-th segmented target data may includeobtaining first to n-th segmented sound data corresponding to the firstto n-th windows; and transforming each of the first to n-th segmentedsound data to spectrogram data to obtain the first to n-th segmentedtarget data.

The spectrogram data may be Mel-spectrogram data.

The first to m-th segmented classification data may be obtained bysequentially inputting m segmented target data corresponding to mwindows that do not overlap each other among the first to n-th windowsinto the urination/non-urination classification model, and the first ton-th segmented urine flow rate data may be obtained by sequentiallyinputting the first to n-th segmented target data into the urine flowrate prediction model.

The urination/non-urination classification model may include at leastfirst input layer, first convolution layer, first hidden layer, andfirst output layer, and the urine flow rate prediction model may includeat least second input layer, second convolution layer, second hiddenlayer, and second output layer, wherein a data form input to the firstinput layer may be same as a data form input to the second input layer.

A size of the first data matrix may be equal to a size of the seconddata matrix.

The obtaining the urination data may include obtaining urinationclassification data using the first to m-th segmented classificationdata; obtaining candidate urine flow rate data using the first to n-thsegmented urine flow rate data; and processing the candidate urine flowrate data using the urination classification data.

The urination data may be obtained by convolution operating theurination classification data and the candidate urine flow rate data.

According to another embodiment of the present specification, a methodof correcting data may be provided, the method may include obtainingactual measurement data group for a plurality of individuals in a datacollection period, wherein the actual measurement data group includes atleast first actual measurement data including urine volume measurementvalue of first individual and second actual measurement data includingurine volume measurement data of second individual; obtaining predictiondata group for urination process of the plurality of individuals in thedata collection period, wherein the prediction data group includes firstprediction data including urine volume prediction value of the firstindividual and second prediction value including urine volume predictionvalue of the second individual, the urine volume prediction value of thefirst individual is generated from first sound data recorded inurination process of the first individual, and the urine volumeprediction value of the second individual is generated from second sounddata recorded in urination process of the second individual; obtainingcompensation value using the actual measurement data group and theprediction data group; obtaining target prediction data for theurination process of target individual after the data collection period,wherein the target prediction data includes urine volume predictionvalue for the urination process of the target individual, and the urinevolume prediction value of the target individual is generated fromtarget sound data recorded in the urination process of the targetindividual; and correcting the target prediction data using thecompensation value.

The obtaining compensation value may include generating data set groupusing the actual measurement data group and the prediction data group;and calculating the compensation value from the data set group using aregression analysis technique.

The data set group may include first data set generated by matching arepresentative value of the urine volume prediction values included inthe first prediction data to a representative value of the urine volumemeasurement values included in the first actual measurement data.

The data set group may include first data set generated by matching arepresentative value of urine volume measurement values in second timeperiod among urine volume measurement values on a plurality of daysincluded in the second actual measurement data to a representative valueof urine volume measurement values in first time period among urinevolume measurement values of a plurality of days included in the firstactual measurement data, and a length of the first time period may beequal to a length of the second time period.

The regression analysis technique may be a linear regression analysistechnique, and the compensation value may be a slope of a functionobtained by using the linear regression analysis.

In the calculating the compensation value from the data set group usingthe regression analysis technique, a value obtained from the firstactual measurement data may be used as either an independent variable ora dependent variable, and a value obtained from the first predictiondata may be used as either the independent variable or the dependentvariable, and the compensation value may be a regression coefficientcalculated through the regression analysis technique.

The obtaining the target prediction data may include generating targeturine flow rate data for the urination process of the target individualusing the target sound data, and calculating urine volume predictionvalue for the urination process of the target individual from the targeturine flow rate data.

The obtaining the actual measurement data group and the obtaining theprediction data group may be performed simultaneously.

According to another embodiment of the present specification, a methodof correcting data may be provided, the method may include obtainingactual measurement data reflecting urine volume measured in urinationprocess of target individual in a first term, wherein the actualmeasurement data includes at least one urine volume measurement value;obtaining sound data group recorded in the urination process of thetarget individual in the first term, wherein the sound data groupincludes first sound data recorded in first urination process; obtainingprediction data using the sound data group, wherein the prediction dataincludes a urine volume prediction value calculated from the first sounddata; obtaining a compensation value using the actual measurement dataand the prediction data; obtaining second sound data recorded in secondurination process of the target individual in second term after thefirst term; calculating a target urine volume prediction value from thesecond sound data; and correcting the target urine volume predictionvalue using the compensation value.

The obtaining compensation value may use regression analysis technique,wherein a value obtained from the actual measurement data is used aseither an independent variable or a dependent variable, and a valueobtained from the prediction data is used as the other of theindependent variable or the dependent variable, and the compensationvalue may be a regression coefficient calculated through the regressionanalysis technique.

The value obtained from the actual measurement data may be a urinevolume measurement value corresponding to the first urination process inthe first term, and the value obtained from the prediction data may bethe urine volume prediction value calculated from the first sound data.

The calculating the target urine volume prediction value from the secondsound data may include generating target urine flow rate data using thesecond sound data; and calculating the target urine volume predictionvalue from the target urine flow rate data.

According to another embodiment of the present specification, a methodof correcting data may be provided, the method may include obtainingactual measurement data reflecting urine volume measured in urinationprocess for target individual in a first term, wherein the actualmeasurement data includes a plurality of urine volume measurement value;obtaining sound data by recording the urination process of the targetindividual in a second term after the first term; obtaining urine volumeprediction data using the sound data; and correcting the urine volumeprediction data using a statistical value of the actual measurementdata; wherein the statistical value is at least one of a mode value, amedian value, an average value, a variance value, or a standarddeviation value of the urine volume measurement values included in theactual measurement data.

The above-described objectives, features, and advantages of the presentspecification will become more apparent from the following detaileddescription in conjunction with the accompanying drawings. However,since the present specification may have various changes and may havevarious exemplary embodiments, specific exemplary embodiments will beexemplified in the drawings and described in detail below.

The same reference numbers throughout the specification indicate thesame components, in principle. In addition, components having the samefunction within the scope of the same idea shown in the drawings of eachexemplary embodiment will be described by using the same referencenumerals, and a redundant description thereof will be omitted.

Numbers (e.g., first, second, etc.) used in a process of describing thepresent specification are only division symbols for dividing onecomponent from other components.

In addition, suffix-like words “module” and “part/unit” for thecomponents used in the following exemplary embodiments are given ormixed in consideration of only the ease of writing the specification,and do not have distinct meanings or roles by themselves.

Unless specifically stated or clear from the context, a term “about” or“around” in reference to a numerical value may be understood to mean astated numerical value and a value up to +/−10% of the numerical value.The term “about” or “around” in reference to a numerical range may beunderstood to mean a range from a value 10% lower than a lower limit ofthe numerical range to a value 10% higher than an upper limit of thenumerical range.

In the exemplary embodiments below, the singular forms are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise.

In the following exemplary embodiments, terms such as “comprise”,“include”, or “have” mean that a feature or a component described in thespecification is present, and the possibility that one or more otherfeatures or components may be added is not precluded.

In the drawings, the size of the components may be exaggerated orreduced for convenience of description. For example, the size andthickness of each component shown in the drawings or views arearbitrarily represented for convenience of description, and the presentdisclosure is not necessarily limited to the illustrated drawings orviews.

Where certain exemplary embodiments are otherwise implementable, aspecific process order may be performed different from the describedorder. For example, two processes described in succession may beperformed substantially and simultaneously, or may be performed in anorder opposite to the described order.

In the following exemplary embodiments, when a film, a region, acomponent, and/or the like are connected to a target, this includes notonly a case where the film, the region, and/or the component aredirectly connected to the target, but also a case where the film, theregion, and/or the component are indirectly connected to the target bymeans of another film, another region, and/or another component that areinterposed therebetween.

For example, in the present specification, when it is said that a film,a region, a component, and/or the like are electrically connected to atarget, this includes not only a case where the film, the region, thecomponent, and/or the like are directly and electrically connected tothe target, but also a case where the film, the region, the component,and/or the like are indirectly and electrically connected to the targetby means of another film, another region, another component, and/or thelike that are interposed therebetween.

The present specification relates to a method of obtaining high accuracyurination information and, more particularly, to a method of obtainingurination information, wherein sound data is obtained by recording soundin a urination process of a person, the sound data is analyzed by usinga urine flow rate prediction model and a urination/non-urinationclassification model classifying urination sections and non-urinationsections, so as to obtain urination data, whereby the urinationinformation is obtained by using the obtained urination data.

Hereinafter, with reference to FIG. 1 , a general environment in whichthe method of obtaining the above-described urination information isperformed will be described.

FIG. 1 is a view illustrating an environment for analyzing urinationinformation according to an exemplary embodiment of the presentspecification. Referring to FIG. 1 , a sound analysis system 1000, arecording device 2000, and an external server 3000 may be used to obtainthe urination information for a urination process.

The sound analysis system 1000 may obtain the urination information onthe basis of data about the urination process. For example, the soundanalysis system 1000 may obtain sound data recorded in the urinationprocess by the recording device 2000, and obtain the urinationinformation by using the obtained sound data. A process in which thesound analysis system 1000 obtains the urination information from thesound data will be described in detail later.

The sound analysis system 1000 may communicate with the external server3000. The sound analysis system 1000 may obtain the above-describedsound data from the recording device 2000 or may also obtain the sounddata from the external server 3000. In addition, the sound analysissystem 1000 may provide the urination information obtained by analyzingthe sound data to the external server 3000. In other words, the soundanalysis system 1000 may obtain the urination information by analyzingthe urination-related sound data received from the outside, and outputor provide the urination information to the outside.

The recording device 2000 may record sound related to urination.Specifically, the recording device 2000 may be worn by a person orinstalled in a space where the person urinates so as to record soundgenerated in the urination process. For example, the recording device2000 may include a wearable device, which is equipped with a recordingfunction, such as a smart watch, a smart band, a smart ring, and a smartneckless, or may include a smart phone, a tablet, a desktop, a laptop, aportable recorder, an installation-type recorder, or the like.

The recording device 2000 may obtain sound data by recording sound inthe urination process.

Here, the sound data may be obtained by digitizing analog acousticsignals for the urination process. For example, the recording device2000 may include an analog to digital converter (ADC) module, and obtainthe sound data from the acoustic signals for the urination process byusing specific sampling rates such as 8 kHz, 16 kHz, 22 kHz, 32 kHz,44.1 kHz, 48 kHz, 96 kHz, 192 kHz, or 384 kHz.

The recording device 2000 may provide the obtained sound data to thesound analysis system 1000 and/or the external server 3000. To this end,the recording device 2000 may perform wired and/or wireless datacommunication with the sound analysis system 1000 and/or the externalserver 3000.

The recording device 2000 may also be used as a means for transmittingurination information to a user. For example, the recording device 2000may obtain the urination information from the sound analysis system 1000and output the urination information to the user.

The external server 3000 may store or provide various data. For example,the external server 3000 may store sound data obtained from therecording device 2000 or urination information obtained from the soundanalysis system 1000. As another example, the external server 3000 mayprovide sound data obtained from the recording device 2000 to the soundanalysis system 1000, and provide urination information obtained fromthe sound analysis system 1000 to the recording device 2000.

Meanwhile, the sound analysis system 1000 and the recording device 2000may be implemented as one device. For example, the sound analysis system1000 may obtain sound data by including a module having a recordingfunction thereof. As another example, components of the sound analysissystem 1000 may be built in the recording device 2000, providing afunction by which the recording device 2000 analyzes sound dataindependently.

Sound Analysis System

Hereinafter, the configuration and operation process of the soundanalysis system 1000 will be described in detail with reference to FIGS.2 and 3 .

FIG. 2 is a view illustrating a configuration of a sound analysis systemaccording to the exemplary embodiment of the present specification.

FIG. 3 is a view illustrating a process by which the configuration ofthe sound analysis system works according to the exemplary embodiment ofthe present specification.

Referring to FIG. 2 , the sound analysis system 1000 may include: apreprocessor 1100, a feature extraction part 1200, a urine flow rateprediction part 1300, a urination/non-urination classification part1400, a urination information extraction part 1500, an input part 1600,an output part 1700, a communication part 1800, and a controller 1900.

The preprocessor 1100 may perform pre-processing on sound data receivedby the sound analysis system 1000. The pre-processing is a processperformed prior to extracting feature values from the sound data, andmay include filtering as described below.

In the preprocessor 1100, filtering for noise removal may be performedon the sound data Here, the filtering may refer to a process ofexcluding noise-related data from the sound data, and to this end, ahigh-pass filter, a low-pass filter, a band-pass filter, and the likemay be used. The filtering of the preprocessor 1100 may be omitted.

Meanwhile, in the preprocessor 1100, windowing, which will be describedlater, may be performed.

The feature extraction part 1200 may extract feature values from thepre-processed sound data. Here, the feature values may refer tonumerical values obtained by quantifying unique features of the sounddata. For example, the feature values may include at least one of a timedomain spectrum magnitude value, a value of spectral centroid, afrequency domain spectrum magnitude value, a frequency domain root meansquare (RMS) value, a spectrogram magnitude value, a Mel-spectrogrammagnitude value, a bispectrum score (BGS), a non-Gaussianity score(NGS), formants frequencies (FF), a value of Log Energy (LogE), a zerocrossing rate (ZCR), a value of kurtosis (Kurt), and a Mel-Frequencycepstral coefficient (MFCC). The feature extraction part 1200 mayconvert the preprocessed sound data and extract feature valuestherefrom. The feature extraction part 1200 may change a conversion formdepending on feature values to be extracted from the sound data. Forexample, when the feature values to be extracted are spectrum values,the feature extraction part 1200 may convert the preprocessed sound datainto spectrum data having a frequency axis. As another example, when thefeature values to be extracted are Mel-spectrogram image values, thefeature extraction part 1200 may convert the preprocessed sound datainto spectrogram image data having a time axis and a frequency axis.When there is provided a plurality of types of feature values to beextracted, the feature extraction part 1200 may convert the preprocessedsound data into various types of data. Hereinafter, for convenience ofdescription, a case where the feature values to be extracted by thefeature extraction part 1200 are values of a Mel-spectrogram image willbe mainly described, but the technical idea of the present specificationis not limited thereto, and may be similarly applied to a case where thefeature values have different forms.

The feature extraction part 1200 may obtain segmented target datathrough windowing for Mel-spectrogram image data obtained by convertingthe pre-processed sound data. Here, the windowing may refer to dividingsound data or converted data between a starting point and an endingpoint thereof by using each window having a time interval of apredetermined size. A specific windowing method will be described later.

The segmented target data obtained through windowing may be understoodas vector data, matrix data, or data having other formats in each windowsection. For example, the segmented target data may be understood as aset of vector data in which values of the above-describedMel-spectrogram image are arranged in a row in each window section. Asanother example, the segmented target data may be understood as a set ofdata in a matrix form for the values of the above-describedMel-spectrogram image in consideration of a time axis and a frequencyaxis in each window section.

Meanwhile, the feature extraction part 1200 may divide the sound datainto different segmented sound data through windowing prior toconverting the sound data to extract the feature values. Alternatively,the windowing is performed in the preprocessor 1100, and the featureextraction part 1200 may be configured to obtain segmented sound datafrom the preprocessor 1100, to extract feature values by converting theobtained segmented sound data into data (e.g., a spectrum, aspectrogram, or a Mel-spectrogram, and the like) including the featurevalues, and to obtain data in a vector form, a matrix form, or otherforms of the extracted feature values as segmented target data. Thefeature extraction part 1200 may transmit the segmented target data tothe urine flow rate prediction part 1300 and/or theurination/non-urination classification part 1400.

The urine flow rate prediction part 1300 may predict a urine flow rateduring urination. For example, the urine flow rate prediction part 1300may calculate predicted urine flow rate values for the sound data byusing a pre-trained urine flow rate prediction model.

The urine flow rate prediction model may refer to a model trained byusing machine learning. Here, the machine learning may be understood asa comprehensive concept including an artificial neural network andfurther including deep-learning. For example, the urine flow rateprediction model may be implemented by an artificial neural networktrained with a training data set in which the data obtained by preciselymeasuring urine flow rates in the urination process is labeled on thesound data obtained by recording sound in the corresponding urinationprocess. The structure and training method of the urine flow rateprediction model will be described in detail later.

The urine flow rate prediction part 1300 may obtain urine flow ratevalues over time in the urination process by using the urine flow rateprediction model and provide the urine flow rate values to the urinationinformation extraction part 1500.

The urination/non-urination classification part 1400 may classifyurination sections and non-urination sections in the urination process.For example, the urination/non-urination classification part 1400 mayobtain classification data, which classifies the urination sections andthe non-urination sections, from the sound data by using a pre-trainedurination/non-urination classification model.

The urination/non-urination classification model may refer to a modeltrained by using machine learning. For example, theurination/non-urination classification model may be implemented by anartificial neural network trained with a training data set in which thedata indicating the urination/non-urination sections obtained by usingurine volume data precisely measured in the urination process is labeledon the sound data obtained by recording sound in the correspondingurination process. The structure and training method of theurination/non-urination classification model will be described in detaillater.

The urination/non-urination classification part 1400 may obtainclassification values, which indicate whether urination ornon-urination, over time in the urination process by using theurination/non-urination classification model, and provide theclassification values to the urination information extraction part 1500.

The urination information extraction part 1500 may obtain urinationinformation for the urination process. For example, the urinationinformation extraction part 1500 may generate urination data by usingthe urine flow rate values obtained from the above-described urine flowrate prediction part 1300 and the classification values obtained fromthe urination/non-urination classification part 1400, and may extractthe urination information from the generated urination data.

Specifically, the urination information extraction part 1500 maygenerate candidate urine flow rate data for the sound-recorded urinationprocess by using the obtained urine flow rate values. In addition, theurination information extraction part 1500 may generate urinationclassification data for the sound-recorded urination process by usingthe obtained classification values. The urination information extractionpart 1500 may obtain urination data based on the candidate urine flowrate data and the urination classification data, which are describedabove, by using a urination data calculation model. Here, the urinationdata may be understood as a set of urine flow rate values over time forthe sound-recorded urination process.

The urination information extraction part 1500 may extract the urinationinformation from the urination data. Here, the urination information inthe urination process may include: a maximum flow rate, an average flowrate, a urine volume, a starting time point and an ending time point ofurination, a urine flow time, a time to maximum urine flow rate, avoiding time (with or without interruption time), and the like.

The preprocessor 1100, the feature extraction part 1200, the urine flowrate prediction part 1300, the urination/non-urination classificationpart 1400, and the urination information extraction part 1500, which aredescribed above, may refer to software programs. For example, processesof noise removal and windowing of the preprocessor 1100, processes ofdata conversion and a target data generation of the feature extractionpart 1200, the urine flow rate prediction model of the urine flow rateprediction part 1300, the urination/non-urination classification modelof the urination/non-urination classification part 1400, and theurination data calculation model of the urination information extractionpart 1500 may be stored in a memory part (not shown) of the soundanalysis system 1000 to be described later in a form of a plurality offunctions or instructions, and may be loaded and performed by thecontroller 1900.

The input part 1600 may receive a user input from a user. The user inputmay be conducted in various forms including a key input, a touch input,and a voice sound input. The input part 1600 is a comprehensive conceptthat includes, for example, not only a traditional keypad, a keyboard,and a mouse, but also a touch sensor for sensing a user's touch andvarious other types of input means for detecting or receiving varioustypes of user inputs.

The output part 1700 may output urination information and provide theurination information to a user. The output part 1700 is a comprehensiveconcept that includes a display for outputting images, a speaker foroutputting sound, a haptic device for generating vibration, and variousother types of output means.

The communication part 1800 may communicate with an external device. Thesound analysis system 1000 may transmit/receive data to and from therecording device 2000 or the external server 3000 through thecommunication part 1800. For example, the sound analysis system 1000 mayprovide urination information to the recording device 2000 and/or theexternal server 3000 through the communication part 1800, and receivesound data from the recording device 2000 and/or the external server3000.

The controller 1900 may control the overall operation of the soundanalysis system 1000. For example, the controller 1900 may obtainurination information from sound data by loading and executing programsrelated to the preprocessor 1100, the feature extraction part 1200, theurine flow rate prediction part 1300, the urination/non-urinationclassification part 1400, and the urination information extraction part1500. The controller 1900 may be implemented as a central processingunit (CPU) or a device similar to the central processing unit accordingto hardware, software, or a combination thereof. In hardware, thecontroller may be provided in a form of an electronic circuit thatperforms a control function by processing electrical signals, and insoftware, the controller may be provided in a form of a program or codesfor driving a hardware circuit.

The sound analysis system 1000 may further include a memory part forstoring various types of information. Various data may be temporarily orsemi-permanently stored in the memory part. Examples of the memory partmay include a hard disk (HDD), a solid state drive (SSD), a flashmemory, a read-only memory (ROM), a random access memory (RAM), etc. Thememory part may be provided in a form embedded in the sound analysissystem 1000 or in a detachable form.

Sound Analysis Process

Hereinafter, each step of the sound analysis process performed in theabove-described sound analysis system 1000 will be described in detailwith reference to FIGS. 4 to 14 .

Window Division Method

FIGS. 4 and 5 are views illustrating a method of dividing sound dataaccording to windows according to the exemplary embodiment of thepresent specification. Hereinafter, a method of dividing windows forsound data will be described for convenience of description, but themethod of dividing windows may be equally applied not only to the sounddata but also to data obtained by converting the sound data in order toextract feature values.

The sound data may have a starting point and an ending point. In thiscase, a length of the sound data may be determined as a time intervalbetween the starting point and the ending point. The length of the sounddata may be determined according to a length of the recorded sound data.

The sound data may be divided into at least one or more of windows. Forexample, the sound data may be divided into at least one or more windowssequentially determined between the starting point and the ending point,and then segmented sound data corresponding to each window may beobtained. Here, the sequential determination of at least one or morewindows means that a plurality of windows is sequentially arranged fromthe starting point to the ending point of the sound data. Meanwhile, atleast one or more windows may be assigned to a specific section betweenthe starting point and the ending point of the sound data in timeseries. Each window may have a predetermined size. The size of a windowmay be determined according to the length of the sound data and thenumber of windows to divide the sound data. In addition, when the numberof windows is plural, the consecutive windows may overlap each other.

For example, referring to FIG. 4 , sound data having 0 seconds as astarting point, and 120 seconds as an ending point may be divided intothe plurality of windows having a size of 2.5 seconds. Specifically,considering a time interval from 37.5 seconds to 40.05 seconds, which isa part of the sound data, a k-th window may correspond to a timeinterval from 37.50 seconds to 40.00 seconds, a (k+1)-th window maycorrespond to a time interval from 37.55 seconds to 40.05 seconds, a(k+2)-th window may correspond to a time interval from 37.60 to 40.10seconds, a (k+3)-th window may correspond to a time interval from 37.65seconds to 40.15 seconds, and a (k+4)-th window may correspond a timeinterval from to 37.70 seconds to 40.20 seconds. As such, consecutivewindows may overlap each other, and an overlapping degree may bedetermined according to resolution of sound data, a sliding degree ofthe consecutive windows, a window size, and the number of windows. Forexample, in FIG. 4 , in the sound data, the resolution may be 0.05seconds, the sliding degree may be 0.05 seconds, the window size may be2.5 seconds, and the overlapping degree of the consecutive windows maybe 2.45 seconds. Meanwhile, the resolution, the sliding degree, and thewindow size of the sound data are not limited to the above-mentionednumerical values, and may be determined such that the resolution isbetween about 0.01 seconds and about 2.00 seconds, the sliding degree isbetween about 0.01 seconds and about 5.00 seconds, and the window sizeis between about 0.05 and about 5.00 seconds. More preferably, theresolution may be determined between about 0.05 seconds and about 1.00seconds, the sliding degree may be determined between about 0.05 secondsand about 2.50 seconds, and the window size may be determined betweenabout 0.10 seconds and about 3.00 seconds.

Considering a data merging process to be described later, data accuracymay increase as the overlapping degree of consecutive windows increases,but data processing speed may be slow due to an increase in a dataprocessing amount. Accordingly, it is necessary to determine theoverlapping degree of consecutive windows in consideration of a prioritybetween the data accuracy and the data processing speed.

As another example, referring to FIG. 5 , sound data having 0 seconds asa starting point, and 120 seconds as an ending point is divided into aplurality of windows having a size of 2.5 seconds, but unlike FIG. 4 ,consecutive windows may not overlap each other. Specifically, referringto a time interval of 37.5 seconds to 45 seconds, the time intervalbeing a part of sound data, the k-th window may correspond to a timeinterval from 37.50 seconds to 40.00 seconds, the (k+1)-th window maycorrespond to a time interval from 40.00 seconds to 42.50 seconds, andthe (k+2)-th window may correspond to a time interval from 42.50 secondsto 45.00 seconds. When sound data is divided into windows so thatconsecutive windows do not overlap, data accuracy may be lowered in thedata merging process to be described later, but data processing speedmay be relatively fast because the data processing amount is reduced.

In dividing the sound data into the plurality of windows, whether theconsecutive windows overlap or not and the overlapping degree at a timewhen the windows overlap may vary depending on target data to beobtained from the sound data.

As an example, in predicting a urine flow rate in the urination processby using sound data, the sound data is divided into the plurality ofwindows in that high accuracy is required for predicted urine flow ratevalues, but as shown in FIG. 4 , it may be preferable to improve theaccuracy by overlapping the consecutive windows.

As another example, in classifying the urination process into urinationsections and non-urination sections by using sound data, the sound datais divided into a plurality of windows in that the determination ofwhether urination or non-urination is relatively easy and thus accuracyis generally high, but it may be preferably that a sliding degree ofeach window is increased to be lower than an overlapping degree ofconsecutive windows in the process of predicting the above-describedurine flow rate, or the consecutive windows are not allowed to overlapas shown in FIG. 5 , so as to improve the data processing speed.

Hereinafter, a case for convenience of description is mainly described,wherein in the process of predicting a urine flow rate, sound data isdivided into the plurality of windows so that consecutive windowsoverlap, and in the process of classifying the urination sections andthe non-urination sections, the sound data is divided into the pluralityof windows so that the overlapping degree of the consecutive windows islower than that of the process of predicting the urine flow rate or theconsecutive windows do not overlap, but the technical idea of thepresent specification is not limited thereto. It is natural that in bothof the process of predicting the urine flow rate and the process ofclassifying the urination sections and the non-urination sections, thesound data may be divided into the plurality of windows so that theoverlapping degree of the consecutive windows is the same, orconversely, in both of the processes, the sound data may be divided intothe plurality of windows so that the consecutive windows do not overlap.

Feature Value Extraction

FIG. 6 is a view illustrating a process of extracting feature valuesfrom the sound data according to the exemplary embodiment of the presentspecification. Here, the feature values are mainly described as beingextracted from an image data of a spectrogram, but naturally, the caseof extracting other types of feature values described above may also beincluded in the technical idea of the present specification.

Sound data may generally include amplitude values over time. The sounddata may be converted, through processing, into spectrum data includingmagnitude values depending on frequencies. Here, the spectrum data maybe obtained by using Fourier transform (FT), Fast Fourier transform(FFT), Discrete Fourier transform (DFT), and Short-time Fouriertransform (STFT).

A spectrogram image may be obtained by using the above-described sounddata and spectrum data. Here, the spectrogram image may be aMel-spectrogram image to which a Mel-scale is applied.

The feature extraction part 1200 may extract data from the spectrogramimage. Data extraction may be understood as a process in whichspectrogram image values respectively corresponding to the plurality ofwindows dividing sound data are extracted.

Obtainment of Target Data for Analysis

FIGS. 7 and 8 are views illustrating a process of obtaining target datato be analyzed according to the exemplary embodiment of the presentspecification.

The target data may refer to a set of data generated by extractingfeature values from the converted sound data. For example, a pluralityof segmented target data may be generated from a spectrogram image ofthe sound data, and target data may be understood as a set of theplurality of segmented target data.

The number of target data, that is, the number of segmented target dataincluded in the target data may be the same as the number of windows.For example, referring to FIG. 7 , when the sound data is divided intofirst to m-th windows (where, m is a natural number greater than orequal to 2), the target data may include first to m-th segmented targetdata respectively corresponding to the first to m-th windows. As anotherexample, when the sound data is divided into first to n-th windows(where, n is a natural number greater than or equal to 2), the targetdata may include first to n-th segmented target data respectivelycorresponding to the first to n-th windows.

Meanwhile, the number of segmented target data included in the targetdata may not be equal to the number of windows. For example, the targetdata may include the number of segmented target data greater than thenumber of windows.

The size of each segmented target data may be determined according to aunit time (or resolution) of a time axis of a spectrogram, the number ofunit sections on a frequency axis, and a window size. For example,assuming that a size of one segmented target data is z*p, where the unittime of the time axis of the spectrogram is 0.05 seconds, the number ofunit sections on the frequency axis is 512, and the window size is 2.5seconds, it may be that z=2.5/0.05=50, and p=512.

As a method of dividing sound data into a plurality of windows in theprocess of generating target data, a method of dividing consecutivewindows so as to overlap as shown in FIG. 7 , and a method of dividingconsecutive windows so as not to overlap as shown in FIG. 8 may be used.

The number of target data used in the urine flow rate prediction modelor the number of target data used in the urination/non-urinationclassification model, which are to be described later, may be differentfrom each other. For example, the number of target data used in theurine flow rate prediction model may be greater than the number oftarget data used in the urination/non-urination classification model.

In this case, the target data used for the urine flow rate predictionmodel and the target data used for the urination/non-urinationclassification model may be generated from the spectrogram image,separately. Here, the size of each segmented target data included in thetarget data used in the urine flow rate prediction model and the size ofeach segmented target data included in the target data used in theurination/non-urination classification model are the same, but the totalnumber of segmented target data used in each model may be different.

Alternatively, at least some of the target data generated from thespectrogram image may be used for the urine flow rate prediction model,and at least some of the target data generated from the spectrogramimage may be used for the urination/non-urination classification model.For example, the entire first to m-th segmented target data generatedfrom the spectrogram image may be used for the urine flow rateprediction model, and the first to n-th segmented target data of thefirst to m-th segmented target data (where, n is greater than or equalto 2 and less than m) may be used for the urination/non-urinationclassification model. Here, the first to m-th segmented target data mayrespectively correspond to the first to m-th windows in which the sounddata or spectrogram image data is divided so that consecutive windowsoverlap. The first to n-th segmented target data may respectivelycorrespond to n data of which the windows among the first to m-thwindows have an overlapping degree smaller than an overlapping degree ofconsecutive windows of the urine flow rate prediction model or do notoverlap each other.

As described above, by varying the number of segmented target data usedin the urine flow rate prediction model and the urination/non-urinationclassification model, the accuracy may be increased in the urine flowrate prediction model and the data processing speed may be increased inthe urination/non-urination classification model.

Meanwhile, the number of segmented target data used in the urine flowrate prediction model or the urination/non-urination classificationmodel may be the same. For example, the first to m-th segmented targetdata generated from the spectrogram image may be input to each of theurine flow rate prediction model and the urination/non-urinationclassification model.

In addition, it is natural that a form (e.g., a data size, a totalnumber, and the like) of the segmented target data used in the urineflow rate prediction model and a form of the segmented target data usedin the urination/non-urination classification model may be differentdepending on the training method.

Urine Flow Rate Prediction Method

Hereinafter, a process of obtaining candidate urine flow rate data fromtarget data will be described with reference to FIGS. 9 and 10 .

FIG. 9 is a view illustrating a process of obtaining urine flow ratedata by using a urine flow rate prediction model according to theexemplary embodiment of the present specification.

Referring to FIG. 9 , the urine flow rate prediction model may receiveinputs of first to m-th segmented target data, and obtain first to m-thsegmented urine flow rate data.

The urine flow rate prediction model is a model trained by using machinelearning, and may use, as the algorithm thereof, at least any one ofk-nearest neighbors, linear regression, logistic regression, a supportvector machine (SVM), a decision tree, a random forest, or a neuralnetwork. Here, at least one of an artificial neural network (ANN), atime delay neural network (TDNN), a deep neural network (DNN), aconvolution neural network (CNN), a recurrent neural network (RNN), or along short-term memory (LSTM) may be selected as the neural networktherefrom.

As an example, referring to FIG. 9 , the urine flow rate predictionmodel may include an input layer, a hidden layer, and an output layer.

Here, the input layer may have an input size of z*p and sequentiallyreceive first to m-th segmented target data. Alternatively, the inputlayer may have an input size of m*z*p and receive first to m-thsegmented target data at once.

Here, when the urine flow rate prediction model uses the CNN algorithm,the hidden layer may include a convolution layer, a pooling layer, and afully-connected layer.

Here, the output layer may output segmented urine flow rate dataincluding urine flow rate values. In this case, the segmented urine flowrate data output from the output layer may correspond to windowscorresponding to the input target data. For example, when firstsegmented target data corresponding to a first window is input to theurine flow rate prediction model and first segmented urine flow ratedata is output, the first segmented urine flow rate data may includeurine flow rate values corresponding to the first window. Specifically,the first segmented urine flow rate data may include predicted urineflow rate values per unit time interval in the first window, that is,for a time interval equal to a window size from a starting point of thesound data, and the number of the predicted urine flow rate value may bedetermined according to the size of the aforementioned target data, oraccording to a unit time of a time axis of a spectrogram (e.g., when theunit time of the time axis of the spectrogram is 0.05 seconds and thewindow size is 2.5 seconds, the number of predicted urine flow ratevalues included in one segmented urine flow rate data is 2.5/0.05=50).

Meanwhile, the urine flow rate prediction model performs a function ofoutputting the predicted urine flow rate values, and unlike theurination/non-urination classification model to be described later, theurine flow rate prediction model does not use an activation function orclassifier, such as a step function, a sigmoid function, or a Softmaxfunction.

FIG. 10 is a view illustrating a method of obtaining candidate urineflow rate data according to the exemplary embodiment of the presentspecification.

The urination information extraction part 1500 may generate candidateurine flow rate data by processing segmented urine flow rate data. Here,the candidate urine flow rate data may include predicted urine flow ratevalues over time in the sound-recorded urination process. Each predictedurine flow rate value included in the candidate urine flow rate data maybe obtained from a plurality of segmented urine flow rate data.Specifically, the predicted urine flow rate values of a specific timeinterval in the candidate urine flow rate data may be obtained by usingthe predicted urine flow rate values (e.g., by using an average value ora median value) corresponding to the specific time interval in each ofthe entire segmented urine flow rate data.

As an example, referring to FIGS. 4 and 10 , when some of data outputthrough the urine flow rate prediction model is called k-th to (k+3)-thsegmented urine flow rate data, and when k-th segmented urine flow ratedata corresponds to the k-th window of 37.50 seconds to 40.00 seconds,(k+1)-th segmented urine flow rate data corresponds to the (k+1)-thwindow of 37.55 seconds to 40.05 seconds, (k+2)-th segmented urine flowrate data corresponds to the (k+2)-th window of 37.60 seconds to 40.10seconds, and (k+3)-th segmented urine flow rate data corresponds to the(k+3)-th window of 37.65 seconds to 40.15 seconds, a predicted urineflow rate value corresponding to 37.65 seconds to 37.70 seconds amongcandidate urine flow rate data for sound data may be calculated by anaverage value obtained by using a predicted urine flow rate value (k, 4)corresponding to 37.65 seconds to 37.70 seconds in the k-th segmentedurine flow rate data, a predicted urine flow rate value (k+1, 3)corresponding to 37.65 seconds to 37.70 seconds in the (k+1)-thsegmented urine flow rate data, a predicted urine flow rate value (k+2,2) corresponding to 37.65 seconds to 37.70 seconds in the (k+2)-thsegmented urine flow rate data, a predicted urine flow rate value(k+3, 1) corresponding to 37.65 seconds to 37.70 seconds in the (k+3)-thsegmented urine flow rate data, and predicted a urine flow rate valuecorresponding to 37.65 seconds to 37.70 seconds in each of the segmentedurine flow rate data before the k-th segmented urine flow rate data.

In the above description, the case where the target data used in theurine flow rate prediction model is obtained on the basis of the sounddata divided into the plurality of windows overlapping with each otherhas been described, but the target data used in the urine flow rateprediction model may also be obtained on the basis of the sound datadivided by the plurality of windows not overlapping with each other. Inthis case, the candidate urine flow rate data may be obtained byconcatenating the segmented urine flow rate data that do not overlapeach other in the time domain through an operation of dataconcatenation, which will be described later.

Meanwhile, the candidate urine flow rate data may be generated by amethod different from the above-described method. For example, thecandidate urine flow rate data may be generated by using spectrum dataobtained by converting the sound data. Specifically, to this end,segmented spectrum data corresponding to each time window is obtained bydividing the sound data into a plurality of non-overlapping timewindows, a frequency domain of the segmented spectrum data is dividedinto a plurality of frequency windows, RMS values in each frequencywindow are extracted as feature values, urine flow rate predictionvalues in the corresponding time window are obtained by using thefeature values in each frequency window, and thus the urine flow rateprediction values respectively obtained from the plurality of timewindows may be obtained as the candidate urine flow rate data.

Urination/Non-Urination Classification Method

FIG. 11 is a view illustrating a process of obtaining classificationdata by using a urination/non-urination classification model accordingto the exemplary embodiment of the present specification.

Referring to FIG. 11 , the urination/non-urination classification modelmay obtain first to n-th segmented classification data by receivingfirst to n-th segmented target data.

The urination/non-urination classification model is a model trained byusing machine learning, and at least any one of k-nearest neighbors,linear regression, logistic regression, a support vector machine, adecision tree, a random forest, or a neural network may be used as thealgorithm. Here, at least one of ANN, TDNN, DNN, CNN, RNN, or LSTM maybe selected as the neural network. The urination/non-urinationclassification model may have the same structure as the above-describedurine flow rate prediction model.

As an example, referring to FIG. 11 , the urination/non-urinationclassification model may include an input layer, a hidden layer, and anoutput layer.

Here, the input layer may have an input size of z*p and receive first ton-th segmented target data, sequentially. Alternatively, the input layermay have an input size of n*z*p and receive first to n-th segmentedtarget data at once.

Here, when the urination/non-urination classification model uses the CNNalgorithm, the hidden layer may include a convolution layer, a poolinglayer, and a fully-connected layer.

Here, the output layer may output segmented classification dataincluding classification values. In this case, the segmentedclassification data output from the output layer may correspond towindows corresponding to the input segmented target data. For example,when first segmented target data corresponding to a first window isinput to the urination/non-urination classification model to outputfirst segmented classification data, the first segmented classificationdata may include predicted classification values corresponding to thefirst window. Specifically, the first segmented classification data mayinclude the predicted classification values per unit time interval inthe first window, i.e., for a time interval having a length equal to thewindow size from a starting point of the sound data, and the number ofthe predicted classification values may be determined according to thesize of the aforementioned segmented target data or the unit time of thetime axis of the spectrogram (e.g., when the unit time of the time axisof the spectrogram is 0.05 seconds and the window size is 2.5 seconds,the number of predicted classification values included in one segmentedclassification data is 2.5/0.05=50).

The predicted classification values output through theurination/non-urination classification model may be values representingthe probability of being a urination section or a non-urination section.For example, it may be understood that the predicted classificationvalues have a value between 0 and 1, and the closer to 1, the higher theprobability of being the urination section, and the closer to 0, thehigher the probability of being the non-urination section. In this case,the urination information extraction part 1500 described later may usethe activation function or the classifier such as the step function, thesigmoid function, or the Softmax function, so as to change the predictedclassification values or a value generated by using the predictedclassification values to a value indicating the urination section or avalue indicating the non-urination section.

Meanwhile, the predicted classification values output through theurination/non-urination classification model may be either a valueindicating a urination section (e.g., 1) or a value indicating anon-urination section (e.g., 0). In order to provide predictedclassification values in a form of a value indicating a specific classas described above, the urination/non-urination classification model mayuse the Softmax function or the Softmax classifier, unlike theaforementioned urine flow rate prediction model.

FIG. 12 is a view illustrating a method of obtaining urinationclassification data according to the exemplary embodiment of the presentspecification.

The urination information extraction part 1500 may generate urinationclassification data by processing a plurality of segmentedclassification data.

First, the urination information extraction part 1500 may generateurination present/absence determination data by processing the pluralityof segmented classification data. The urination presence/absencedetermination data may include values related to whether urination ornon-urination over time in the sound-recorded urination process. Here,the values, related to whether urination or non-urination, included inthe urination presence/absence determination data may be understood as aprobability value of being a urination section or a probability value ofbeing a non-urination section. Each of the values included in theurination presence/absence determination data may be obtained from theplurality of segmented classification data. Specifically, the valuecorresponding to a specific window (or a specific time interval) in theurination presence/absence determination data may be obtained by usingpredicted classification values (e.g., by using an average value or amedian value) corresponding to the specific window (or the specific timeinterval) in each of the entire segmented classification data.

As an example, referring to FIG. 12 , when the size of a window thatdivides sound data is 2.5 seconds and a sliding degree is 1.25 seconds,k-th to (k+3)-th segmented classification data, which are some of thedata output through the urination/non-urination classification model,may respectively correspond to a time interval from 37.50 seconds to40.00 seconds, a time interval from 38.75 seconds to 41.25 seconds, atime interval from 40.00 seconds to 42.50 seconds, and a time intervalfrom 41.25 seconds to 43.75 seconds. A value in a specific window of theurination presence/absence determination data may be obtained by anaverage value or a median value of predicted classification valuescorresponding to the specific window in each of the segmentedclassification data.

The urination present/absence determination data may be used todetermine the presence or absence of a urination section, which will bedescribed later.

The urination information extraction part 1500 may obtain urinationclassification data for sound data by processing urinationpresence/absence determination data. For example, the urinationinformation extraction part 1500 may apply the step function, thesigmoid function, the Softmax function, or the like to the urinationpresence/absence determination data, so as to generate urinationclassification data that includes a value (e.g., 1) indicating aurination section or a value (e.g., 0) indicating a non-urinationsection.

FIG. 13 is a view illustrating a method of obtaining urinationclassification data according to another exemplary embodiment of thepresent specification.

The urination information extraction part 1500 may generate urinationclassification data by processing the segmented classification data.Here, the urination classification data may include predictedclassification values over time in the sound-recorded urination process.Each of the predicted classification values included in the urinationclassification data may be obtained by concatenating the plurality ofsegmented classification data. Specifically, predicted classificationvalues of a specific time interval in the urination classification datamay be obtained as predicted classification values corresponding to thespecific time interval in segmented classification data corresponding toa window including the specific time interval. Here, each predictedclassification value may be a probability value related to a urinationsection or a non-urination section (e.g., it may be understood as avalue between 0 and 1, and the closer to 1, the higher a probability ofbeing a urination section). Alternatively, a predicted classificationvalue may be a value indicating a urination section (e.g., 1) or a valueindicating a non-urination section (e.g., 0).

As an example, referring to FIGS. 5 and 13 , when some of the dataoutput through the urination/non-urination classification model iscalled k-th to (k+2)-th segmented classification data, and when a k-thsegmented classification data corresponds to a k-th window of 37.50seconds to 40.00 seconds, a (k+1)-th segmented classification datacorresponds to a (k+1)-th window of 40.00 seconds to 42.50 seconds, anda (k+2)-th segmented classification data corresponds to a (k+2)-thwindow of 42.50 seconds to 45.00 seconds, the predicted classificationvalues of urination classification data for sound data may be obtainedsuch that predicted classification values corresponding to 37.50 secondsto 40.00 seconds are predicted classification values (0, . . . , 1, . .. , 1) of the k-th segmented classification data, predictedclassification values corresponding to 40.00 seconds to 42.50 secondsare predicted classification values (1, . . . , 1, 1 . . . 1) of the(k+1)-th segmented classification data, and predicted classificationvalues corresponding to 42.50 seconds to 45.00 seconds are predictedclassification values (1, 0, 0, 1, . . . , 0) of the (k+2)-th segmentedclassification data.

Meanwhile, urination classification data may be generated by a methodother than the above-described method.

For example, urination classification data may be generated by usingspectrum data obtained by converting sound data. Specifically, thesegmented spectrum data corresponding to respective time window isobtained by dividing the sound data into a plurality of time windows,the frequency domain of the segmented spectrum data is divided into aplurality of frequency windows, RMS values in each frequency window areextracted as feature values, whether the corresponding time window isthe urination section or the non-urination interval is determined byusing the feature values in each frequency window, and thus the resultmay be obtained as the urination classification data.

As another example, urination classification data may be generated byusing sound data. Specifically, zero-crossing rates are extracted asfeature values for the sound data, and urination sections andnon-urination sections are determined in the sound data on the basis ofthe extracted feature values, and thus the result may be obtained as theurination classification data.

Urination Data Obtainment Method

Hereinafter, a method of generating urination data will be describedwith reference to FIGS. 14 and 15 .

FIGS. 14 and 15 are views illustrating a method of obtaining theurination data by using the candidate urine flow rate data and theurination classification data according to the exemplary embodiment ofthe present specification.

The urination information extraction part 1500 may calculate urinationdata by using candidate urine flow rate data and urinationclassification data. The urination data may be obtained in a formincluding urine flow rate prediction values over time for asound-recorded urination process. For example, referring to FIG. 14 ,the urination data may be obtained by performing convolution on thecandidate urine flow rate data and the urination classification data.For another example, urination data may be obtained by multiplying eachof the urine flow rate prediction values over time included in candidateurine flow rate data by each of classification prediction values overtime included in urination classification data. In this case, theurination data may be expressed as a matrix of discrete values.

The urination information extraction part 1500 may extract urinationinformation by using the obtained urination data. For example, theurination information extraction part 1500 may obtain the largest valueamong the predicted urine flow rate values included in the obtainedurination data as the maximum urine flow rate value, and calculate aurine volume for each section or a total urine volume through discreteintegration. Meanwhile, referring to FIG. 14 , the urination informationextraction part 1500 may obtain a urination graph for a sound-recordedurination process by processing the urination data (e.g., by smoothing,interpolation, and the like), and calculate, from the urination graph, amaximum urine flow rate value, an average urine flow rate value, avoiding time, a urine volume for each section, a total urine volume, andthe like.

The urination data may be obtained by processing each of the candidateurine flow rate data and the urination classification data, and thenperforming calculation on the processed data. For example, referring toFIG. 15 , the urination information extraction part 1500 may generate acandidate urine flow rate graph for a sound-recorded urination processby processing candidate urine flow rate data, generate aurination/non-urination section graph for the sound-recorded urinationprocess by processing urination classification data, and obtain aurination graph for the sound-recorded urination process by performing aconvolution calculation on the generated candidate urine flow rate graphand the generated urination/non-urination section graph. Here, theoperation of smoothing or interpolation may be performed whileprocessing the candidate urine flow rate data or the urinationclassification data.

Sound Analysis Method

Hereinafter, a method of analyzing sound to obtain urination informationfor a urination process by using the sound analysis system 1000 withreference to FIGS. 16 and 17 will be described in steps, but the contentoverlaps with the above-mentioned parts will be omitted.

Proceeding Sound Analysis without Determining Whether Urination SectionExists

FIGS. 16 and 17 are flowcharts illustrating a method of analyzing theurination information according to the exemplary embodiment of thepresent specification.

Referring to FIG. 16 , the method of analyzing sound may include: stepS110 of obtaining sound data; step S120 of processing the obtained sounddata; step S130 of predicting a urine flow rate; step S140 ofclassifying urination/non-urination section; step S150 of obtainingurination data; and step S160 of outputting urination information.

In step S110, a sound analysis system 1000 may obtain sound data. Thesound analysis system 1000 may obtain the sound data recorded in aurination process of a person from a recording device 2000.Alternatively, the sound analysis system 1000 may obtain arbitrary sounddata from outside, but the corresponding sound data may not be datarecorded in the urination process.

In step S120, the sound analysis system 1000 may process the obtainedsound data. The sound analysis system 1000 may process the obtainedsound data by using a preprocessor 1100 and a feature extraction part1200. Since the method of processing the sound data has been describedabove, a detailed description thereof will be omitted.

In step S130, the sound analysis system 1000 may predict a urine flowrate. The sound analysis system 1000 may obtain a segmented urine flowrate data group, which corresponds to each window dividing the sounddata, from data obtained by processing the sound data by using the urineflow rate prediction part 1300, and may obtain candidate urine flow ratedata from the segmented urine flow rate data group by using theurination information extraction part 1500. The candidate urine flowrate data may include values predicting the urine flow rate in theentire sound data. Since the process of generating the candidate urineflow rate data has been described above, specific details thereof willbe omitted.

In step S140, the sound analysis system 1000 may classify urinationsections and non-urination sections. The sound analysis system 1000 mayobtain a segmented classification data group, which corresponds to eachwindow dividing the sound data, from data obtained by processing thesound data by using the urination/non-urination classification part1400, and may obtain urination classification data from the segmentedclassification data group by using the urination information extractionpart 1500. The urination classification data may include classificationvalues for classifying the urination sections and the non-urinationsections in the overall sound data. Since the process of generating theurination classification data has been described above, specific detailsthereof will be omitted.

The above-described step S130 of predicting the urine flow rate and thestep S140 of classifying the urination sections and the non-urinationsections may be performed in parallel or sequentially.

In step S150, the sound analysis system 1000 may obtain urination data.The sound analysis system 1000 may obtain the urination data from thecandidate urine flow rate data and the urination classification data byusing the urination information extraction part 1500. It may beunderstood that the urination data includes, in the sound data, urineflow rate prediction values selected by the urination classificationdata from among the urine flow rate prediction values included in thecandidate urine flow rate data. Since the process of generating theurination data has been described above, specific details thereof willbe omitted.

In step S160, the sound analysis system 1000 may output urinationinformation. The sound analysis system 1000 may extract the urinationinformation from the urination data by using the urination informationextraction part 1500, may provide the extracted urination information toa user through an output part 1700, or may provide to a recording device2000 and/or an external server 3000. Here, as shown in FIG. 3 , theoutput urination information may include a urine volume, a maximum urineflow rate, an average urine flow rate, and a voiding time.

Proceeding Sound Analysis after Determining Whether Urination SectionExists

Meanwhile, the method of analyzing urination information may performdetermination of whether urination exists prior to predicting a urineflow rate in order to improve efficiency in the data processing process.

Referring to FIG. 17 , the method of analyzing urination information mayinclude: step S210 of obtaining sound data; step S220 of processing theobtained sound data; step S230 of classifying urination sections and thenon-urination sections; step S240 of determining whether a urinationsection exists; step S250 of predicting a urine flow rate; step S260 ofobtaining urination data; and step S270 of outputting urinationinformation. Here, since step S210 of obtaining the sound data, stepS220 of processing the obtained sound data, and step S270 of outputtingthe urination information are described above in FIG. 16 , redundantcontent will be omitted.

In step S230, a sound analysis system 1000 may classifyurination/non-urination sections prior to predicting a urine flow rate.By using the urination/non-urination classification part 1400 and theurination information extraction part 1500, the sound analysis system1000 may obtain urination classification data from data obtained byprocessing the sound data.

In step S240, the sound analysis system 1000 may determine whether aurination section exists. The sound analysis system 1000 may determinewhether the sound data is data obtained by recording sound in theurination process by using the urination classification data obtained instep S230 of classifying the urination/non-urination. For example, thesound analysis system 1000 may determine whether a urination sectionexists between a starting point and an ending point of the sound data.

As an example, the sound analysis system 1000 may determine whether theurination process is reflected in the sound data on the basis of theabove-described urination presence/absence determination data. Forconvenience of explanation, values included in the urinationpresence/absence determination data are referred to as determinationvalues. The sound analysis system 1000 may determine that the urinationprocess is reflected in the sound data when at least one of thedetermination values included in the urination presence/absencedetermination data is greater than or equal to a threshold value.Alternatively, the sound analysis system 1000 may determine that theurination process is reflected in the sound data when a ratio of anydetermination value greater than or equal to the threshold value amongthe determination values included in the urination presence/absencedetermination data is greater than or equal to a predetermined ratio(e.g., 5% to 50%). The threshold value for determining whether aurination section exists may be set in various ways. For example, thethreshold value may be determined as a value between 0.30 and 0.95. Morepreferably, the threshold value may be determined as a value between0.50 and 0.80.

As another example, the sound analysis system 1000 may determine whetherthe urination process is reflected in the sound data on the basis of theurination classification data. Specifically, when the number ofclassification values indicating the urination sections in the urinationclassification data is greater than or equal to a predetermined number,the sound analysis system 1000 may determine that the sound related tothe urination process is reflected in the sound data. Alternatively, thesound analysis system 1000 may calculate a ratio of the number ofclassification values indicating the urination sections to the number ofclassification values indicating the non-urination sections in theurination classification data, and when the calculated ratio is greaterthan or equal to the predetermined ratio, the sound analysis system 1000may determine that the urination sections exist.

According to the above-described determination method in step S240 ofdetermining whether a urination section exists, the sound analysissystem 1000 may generate only urination presence/absence determinationdata, or generate both of the urination presence/absence determinationdata and the urination classification data.

When it is determined that a urination section exists in the sound data,the sound analysis system 1000 may enter step S260 of predicting theurine flow rate, and when it is determined that a urination section doesnot exist in the sound data, the sound analysis system 1000 mayterminate the sound analysis. In a case of determining whether aurination section exists by using the urination presence/absencedetermination data, when it is determined that a urination section doesnot exist in the sound data, the sound analysis system 1000 may notgenerate urination classification data from the urinationpresence/absence determination data.

In step S250, when it is determined that a urination section exists inthe sound data, the sound analysis system 1000 may predict a urine flowrate. The sound analysis system 1000 may obtain a segmented urine flowrate data group, which corresponds to each window dividing the sounddata, from data obtained by processing the sound data by using the urineflow rate prediction part 1300, and may obtain candidate urine flow ratedata from the segmented urine flow rate data group by using theurination information extraction part 1500. The candidate urine flowrate data may include values predicting urine flow rates from theoverall sound data. Since the process of generating the candidate urineflow rate data has been described above, specific details will beomitted.

Here, the processed sound data used in the urine flow rate predictionpart 1300 may be different from the data used in step S230 ofclassifying the urination sections and the non-urination sections. Forexample, the data used by the urine flow rate prediction part 1300 isdata corresponding to each window when the sound data is divided into mwindows so that consecutive windows overlap. The data used in step S230of classifying the urination sections and the non-urination sections isdata corresponding to each window when the sound data is divided into nwindows so that consecutive windows do not overlap, wherein n may besmaller than m.

In step S260, the sound analysis system 1000 may obtain urination data.The sound analysis system 1000 may obtain the urination data by usingthe urination classification data obtained in step S230 of classifyingthe urination sections and the non-urination sections and the candidateurine flow rate data obtained in step S250 of predicting the urine flowrate.

As described above, in the method of analyzing the urinationinformation, by determining whether a urination section exists prior toperforming step S250 of predicting the urine flow rate, unnecessaryanalysis of the data may be prevented in advance when theurination-related sound is not reflected in the sound data to beanalyzed.

Sound Analysis System Effect

Hereinafter, in the method of analyzing the urination information byusing the sound analysis system 1000, practical effects in a case ofperforming the urination/non-urination classification process will bedescribed.

FIG. 18 is a view illustrating a graph for comparing a result of a casewhen the urination/non-urination classification model is not used and aresult of a case when the urination classification model is usedaccording to the exemplary embodiment of the present specification.

In the method of analyzing the urination information, when theurination/non-urination classification model is not used, the urinevolume may be excessively predicted compared to that of a case where theurination/non-urination classification model is used. For example, inFIG. 18(a) to FIG. 18(d), the urine volumes are measured to be larger inthe case where the urination/non-urination classification model is notapplied than in the case where the urination/non-urinationclassification model is applied. In particular, in the case of FIG.18(a) and FIG. 18(c), it may be confirmed that differences between urinevolumes in the case where the urination/non-urination classificationmodel is applied and urine volumes in the case where the urinationclassification model is not applied are about twice as large.

In the method of analyzing the urination information, in the case whenthe urination/non-urination classification model is not used, themaximum urine flow rate may be excessively predicted compared to that ofthe case when the urination/non-urination classification model is used.For example, in FIG. 18(a) and FIG. 18(c), the maximum urine flow rate(Qmax) is measured to be larger when the urination/non-urinationclassification model is not applied than that of the case when theurination/non-urination classification model is applied. For men, sincethe maximum urine flow rate value is a significantly important factor indiagnosing a urination function, a non-excessive measurement of themaximum urine flow rate is an important issue.

In the method of analyzing the urination information, when theurination/non-urination classification model is not used, the accuratemeasurement of a urine flow time and a voiding time is more difficultthan when the urination/non-urination classification model is used. Forexample, in FIGS. 18(a) and 18(c), it may be confirmed that when theurination/non-urination classification model is not applied, since thereis a plurality of peak values, it is difficult to measure starting andending points of urination and a resulting voiding time, whereas, whenthe urination/non-urination classification model is applied, since thestarting and ending points of the urination are clear, the total voidingtime may be accurately calculated.

As described above, it is absolutely essential to use theurination/non-urination classification model in order to accuratelymeasure urination information such as a urine volume, a maximum urineflow rate, and a voiding time.

Model Training Method

Hereinafter, with reference to FIGS. 19 and 20 , a process of trainingthe urine flow rate prediction model and the urination/non-urinationclassification model described above will be described in detail.

Urine Flow Rate Prediction Model Training Method

FIG. 19 is a flowchart illustrating a process of training the urine flowrate prediction model according to the exemplary embodiment of thepresent specification.

Referring to FIG. 19 , a urine flow rate prediction model may include:step S310 of collecting sound data in a urination process; step S320 ofgenerating feature data by processing the collected sound data; stepS330 of measuring urine flow rate values at a preset period in theurination process; step S340 of generating actual measurement urine flowrate data by using the measured urine flow rate values; step S350 ofgenerating a training data set by labeling the actual measurement urineflow rate data on the feature data; and step S360 of generating theurine flow rate prediction model by using the training data set.

Hereinafter, each step will be described. Each step of the method oftraining the urine flow rate prediction model may be performed by aperson or a separate processor.

In step S310, sound data for a urination process may be collected in aprocess of training a urine flow rate prediction model. The sound datamay be obtained by recording sound in the urination process of a person.Meanwhile, in step S310 of collecting the sound data, the sound datathat does not include the sound in the urination process of the personmay be collected in order to improve the performance of the urine flowrate prediction model. The above-described recording device 2000 may beused to collect the sound data.

In step S320, feature data may be generated from the sound datacollected in the process of training the urine flow rate predictionmodel. The feature data may refer to data including feature valuesextracted from the sound data. Here, the feature values may include atleast one of a spectrum time domain spectrum magnitude value, afrequency domain spectrum magnitude value, a frequency domain root meansquare (RMS) value, a spectrogram magnitude value, a Mel-spectrogrammagnitude value, a bispectrum score, a non-Gaussianity score, formantsfrequencies, a value of Log Energy, a zero crossing rate, a value ofkurtosis, and a Mel-Frequency cepstral coefficient.

The feature data may be divided into a plurality of windows. Forexample, the feature data may include a plurality of segmented featuredata respectively corresponding to the plurality of windows sequentiallydetermined between a starting point and an ending point of the sounddata or the feature data.

Since a process of generating the feature data and the segmented featuredata is the same as the process of generating the target data and thesegmented target data, specific details will be omitted.

In step S330, in the process of training the urine flow rate predictionmodel, urine flow rate values may be measured at a preset period for theurination process. For example, the urine flow rate values in theurination process may be measured by using a scale. For example, in theurination process, the urine flow rate values may be measured by using atoilet with a built-in scale or a toilet mounted on the scale.Specifically, as the urination process is performed through the toiletwith the built-in scale or the toilet mounted on the scale, urinevolumes over time may be measured by using changes over time of a weightmeasured on the scale, and urine flow rate values over time may bemeasured from data on the urine volumes. In this case, a period in whichthe urine flow rate values measured or a period in which the urinevolumes are measured may correspond to the resolution of the sound data.

In step S340, actual measurement urine flow rate data may be generatedby using the urine flow rate values measured in step S330 of measuringthe urine flow rate. The actual measurement urine flow rate data mayinclude urine flow rate values measured or calculated at a presetperiod. The actual measurement urine flow rate data may be divided intoa plurality of windows.

For example, the actual measurement urine flow rate data may include aplurality of segmented actual measurement urine flow rate datarespectively corresponding to the plurality of windows sequentiallydetermined between a measurement starting time point and a measurementending time point of the urination process. In this case, the size of awindow dividing the actual measurement urine flow rate data may be thesame as the size of the window dividing the above-described featuredata. In addition, the number of segmented actual measurement urine flowrate data included in the actual measurement urine flow rate data may bethe same as the number of segmented feature data included in theaforementioned feature data.

Steps S310 and S320 of collecting the above-described sound data andgenerating the feature data from the collected sound data may beperformed at a previous time point or a later time point relative tosteps S330 and S340 of measuring the urine flow rate values in theurination process and generating the actual measurement urine flow ratedata by using the measured urine flow rate values, or may be performedin parallel.

In step S350, in the process of training the urine flow rate predictionmodel, the actual measurement urine flow rate data may be labeled on thefeature data to generate a training data set. The feature data and theactual measurement urine flow rate data corresponding to each windowsection may be labeled with each other. For example, first training datamay be generated by labeling first segmented feature data included inthe feature data and configured to correspond to a first window sectionwith first segmented actual measurement urine flow rate data included inthe actual measurement urine flow rate data and configured to correspondto the first window section.

In step S360, the urine flow rate prediction model may be generated byusing the aforementioned training data set. The urine flow rateprediction model may be trained by using the training data set. Thetrained urine flow rate prediction model may be used to obtain thesegmented urine flow rate data by using the above-described segmentedtarget data.

Urination/Non-Urination Classification Model Training Method

FIG. 20 is a flowchart illustrating a process of training theurination/non-urination classification model according to the exemplaryembodiment of the present specification.

Referring to FIG. 20 , the method of training urination/non-urinationclassification model may include: step S410 of collecting sound data ina urination process; step S420 of generating feature data by processingthe collected sound data; step S430 of determining whether urination ornon-urination at a preset period in the urination process; step S440 ofgenerating urination/non-urination determination data; step S450 ofgenerating a training data set by labeling urination/non-urinationdetermination data on the feature data; and step S460 of generating aurination/non-urination classification model by using the training dataset.

Hereinafter, each step will be described. Each step of the method oftraining urination/non-urination classification model may be performedby a person or a processor. Here, since step S410 of collecting thesound data and step S420 of generating the feature data by processingthe collected sound data are respectively the same as steps S310 andS320 described in FIG. 19 , specific details will be omitted.

In step S430, in a process of training the urination/non-urinationclassification model, whether urination or non-urination may bedetermined for the urination process. For example, a urination ornon-urination section may be determined for the urination process byusing the sound data recorded in the urination process. Specifically,the intensity in each time interval in the sound data recorded in theurination process may be compared with a predetermined value, so as todetermine a corresponding section as a urination section or anon-urination section. For another example, a urination section and anon-urination section may be determined in the urination process byusing urine volume data obtained by measuring urine volumes over time ineach urination process. In measuring a urine volume for the urinationprocess, the above-described method of measuring the urine volume may beused.

In step S440, urination/non-urination determination data may begenerated by using the urination sections and the non-urinationsections, which are determined in step S430 of determining whetherurination or non-urination. The urination/non-urination determinationdata may include determination values determined at a preset period inthe time domain.

The urination/non-urination determination data may be divided into aplurality of windows. For example, the urination/non-urinationdetermination data may include a plurality of segmented determinationdata respectively corresponding to the plurality of windows sequentiallydetermined between a measurement starting time point and a measurementending time point of the urination process. In this case, the size of awindow dividing the urination/non-urination determination data may bethe same as the size of the window dividing the feature data generatedin step S420. In addition, the number of segmented determination dataincluded in the urination/non-urination determination data may be thesame as the number of segmented feature data included in the featuredata generated in step S420.

Steps S410 and S420 of collecting the above-described sound data andgenerating the feature data from the collected sound data may beperformed at a previous time point or a later time point relative tosteps S430 and S440 of determining whether urination or non-urination inthe urination process and generating the determination data by using thedetermination values, or may be performed in parallel.

In step S450, in the process of training the urination/non-urinationclassification model, the urination/non-urination determination data maybe labeled on the feature data to generate a training data set. Thefeature data and the urination/non-urination determination datarespectively corresponding to each window section may be labeled witheach other. For example, first training data may be generated bylabeling the first segmented feature data included in the feature dataand configured to correspond to a first window section with thesegmented determination data included in the urination/non-urinationdetermination data and configured to correspond to the first windowsection.

In step S460, a urination/non-urination classification model may begenerated by using the above-described training data set. Specifically,the urination/non-urination classification model may be trained by usingthe training data set. The trained urination/non-urinationclassification model may be used to obtain the segmented classificationdata by using the above-described segmented target data.

Hereinafter, a method of correcting data, wherein urination data orurination information, which is obtained by using sound data, iscorrected will be described. Here, note in advance that the urinationdata or the urination information, which is corrected by applying themethod of correcting the data, may be urination data or urinationinformation, which is not only obtained by the method of obtaining highaccuracy urination information described through FIGS. 1 to 20 but alsoobtained by other methods.

In addition, in the following, for convenience of explanation, a casewhere urination data or urination information to be corrected ispredicted values related to urine volumes is mainly described, but thetechnical idea of the present specification is not limited thereto, anda case of correcting predicted values for the above-described maximumurine flow rate, average urine flow rate, error time, or voiding timemay be similarly applied thereto.

Method of Correcting Urination Data

FIG. 21 is a view illustrating actual measurement data and predictiondata according to the exemplary embodiment of the present specification.

The actual measurement data may include urine volume measurement valuesmeasured in the urination process of any one individual (e.g., a generalpublic, a patient, a subject, or the like) for a predetermined period oftime. For example, the actual measurement data may include the urinevolume measurement values measured in the urination process of oneperson during one day. As another example, the actual measurement datamay include urine volume measurement values measured in the urinationprocess of one person for a plurality of days. The urine volumemeasurement values may be obtained in various ways. For example, theurine volume measurement values may be obtained by using a measurementdevice such as a paper cup or a graduated cup, a portable householdurine flow rate measurement device, a urine flow rate measurement deviceused in a medical institution, and a measurement device such as a scale.

The number of urine volume measurement values included in the actualmeasurement data may be the same as the number of urination processes ofa corresponding individual.

The prediction data may include urine volume prediction valuescalculated in the urination process of any one individual for apredetermined period of time. For example, the prediction data mayinclude urine volume prediction values obtained by using sound dataobtained accordingly by recording sound in the urination process of aperson during one day. As another example, the prediction data mayinclude urine volume prediction values obtained by using sound dataobtained accordingly by recording sound in the urination process of theperson for a plurality of days. The urine volume prediction values maybe obtained in various ways. For example, the urine volume predictionvalues may refer to values derived, from a process of estimating urineflow rates or urine volumes, by analyzing the sound data obtained byrecording sound in the urination process of a person by any method.Specifically, the urine volume prediction values may be obtained byusing the aforementioned sound analysis system 1000. In this case, theurine volume prediction values may be calculated from the candidateurine flow rate data obtained through the urine flow rate predictionpart 1300 and the urination information extraction part 1500, or may becalculated from the urination data obtained through the urine flow rateprediction part 1300, the urination/non-urination classification part1400, and the urination information extraction part 1500.

The number of urine volume prediction values included in the predictionmeasurement data may be the same as the number of urination processes ofthe corresponding individual.

Referring to FIG. 21 , actual measurement data (marked with X) andprediction data (marked with •) for the urination process of any oneperson for any period of time (e.g., from January 29 to February 5) aregraphically shown, and the prediction data has a relatively large valuewhen being compared with the actual measurement data. Specifically, theurine volume measurement values for the urination process from January29 to February 1 are within about 100 ml to about 500 ml, whereas theurine volume prediction values for the urination process from February 2to February 5 are within about 300 ml to about 1000 ml. In other words,when the urine volumes are predicted by using the sound data, thepredicted values may have larger values than those of the actuallymeasured urine volumes, and this needs to be corrected. Hereinafter, acase in which predicted urine volumes have larger values than actuallymeasured urine volumes will be mainly described, but the technical ideaof the present specification is not limited thereto, and may also besimilarly applied to a case where the actually measured urine volumeshave larger values than the predicted urine volumes.

FIG. 22 is a view illustrating the actual measurement data, theprediction data, and corrected prediction data according to theexemplary embodiment of the present specification.

Referring to FIG. 22 , corrected prediction data (marked with □)obtained by correcting prediction data is displayed in a graph shown inFIG. 21 . Specifically, in FIG. 22 , the corrected prediction valuesobtained by scaling the urine volume prediction values from February 2to February 5 are displayed, and corrected prediction values may havevalues within about 100 ml to about 400 ml.

In general, a person's urine volume is proportional to the size of thebladder of a person, and even in different urination processes, eachurine volume value may have a value within a certain range.

In consideration of such circumstances, prediction data for theurination process of the person may be corrected by using actualmeasurement data for the urination process of the person. For example,when the urine volume measurement values of the actual measurement datahave a first range and the urine volume prediction values of theprediction data have a second range, the urine volume prediction valuesof the prediction data may be corrected such that the second range isthe same as or similar to the first range.

Meanwhile, a range of values of the person's urine volume may bemaintained within a certain period. In other words, since the person'surine volume may be determined depending on the size of the bladder ofthe person, the range of values of the person's urine volume may bemaintained unless there is a circumstance such as a change in physicalcharacteristics, a treatment, an occurrence of a disease, or the like.

Considering such a circumstance, on the basis of data at any one pointin time, data at another point in time may be corrected for theurination-related data collected at different points in time. Forexample, current or future prediction data may also be corrected byusing past actual measurement data, the current or future predictiondata may also be corrected by using the past actual measurement data andpast prediction data, and the reverse is also possible. Meanwhile, theurination-related data may be collected once more when there is aspecial circumstance described above, such as the change in physicalcharacteristics, the treatment, or the occurrence of the disease.

Hereinafter, with reference to FIGS. 23 to 26 , a method of correctingthe prediction data for the urine volumes collected in the past or to becollected in the future by using the actual measurement data for theurine volumes previously collected will be described.

FIG. 23 is a flowchart illustrating a method of correcting dataaccording to the exemplary embodiment of the present specification.

Referring to FIG. 23 , the method of correcting the data may include:step S510 of obtaining actual measurement data; step S520 of obtainingprediction data; step S530 of obtaining a compensation value by usingthe actual measurement data and the prediction data; step S540 ofobtaining target prediction data; and step S550 of correcting the targetprediction data by using a correction value.

At least a part of each step in the method of correcting the data may beperformed through the controller 1900 or a separate processor of theaforementioned sound analysis system 1000.

As an example, at least a part of the method of correcting the data maybe performed by a data correction part. In hardware, the data correctionpart may be provided in a form of an electronic circuit that performs acontrol function by processing electrical signals, or in software, thedata correction part may be provided in a form of a program or codes fordriving a hardware circuit. The data correction part may receive datafrom the outside and perform the method of correcting the data by thereceived data, or on the received data.

Hereinafter, each step will be described in detail.

In step S510, in the method of correcting the data, actual measurementdata may be obtained. As described above, the actual measurement datarefers to data obtained by measuring urine volumes in the urinationprocess of any one individual. Specifically, the actual measurement datamay include a urine volume measurement value measured for each urinationprocess of one person.

In step S510 of obtaining the actual measurement data, an actualmeasurement data group for a plurality of individuals may be obtained.For example, the above-described actual measurement data group mayinclude first to s-th actual measurement data (where, s is a naturalnumber greater than or equal to 2) for first to s-th individuals. Eachactual measurement data may include at least one or more urine volumemeasurement values related to the urination process of the correspondingindividual. As a result, obtaining of the actual measurement data groupmay be understood as collecting the urine volume measurement valuesmeasured in the urination process of the plurality of individuals.

In step S520, prediction data may be obtained in the method ofcorrecting the data. As described above, the prediction data refers todata in which urine volumes are predicted in the urination process ofany one individual. Specifically, the prediction data may include urinevolume prediction values calculated by using the sound data recorded inthe urination process of one person.

In step S520 of obtaining the prediction data, a prediction data groupfor a plurality of individuals may be obtained. For example, theprediction data group may include first to t-th prediction data (where,t is a natural number greater than or equal to 2, which may be the sameas or different from s) for first to t-th individuals. Each predictiondata may include at least one or more urine volume prediction valuesrelated to the urination process of a corresponding individual. As aresult, obtaining of the prediction data group may be understood ascollecting the urine volume prediction values calculated in theurination process of the plurality of individuals.

Step S510 of obtaining the actual measurement data and step S520 ofobtaining the prediction data may be performed sequentially or inparallel.

In step S530, in the method of correcting the data, a compensation valuemay be obtained by using the actual measurement data and the predictiondata. The compensation value may be understood as a value for correctingthe collected prediction data or the prediction data to be collectedlater.

The compensation value may be calculated by applying a data analysistechnique to both of the actual measurement data obtained in step S510of obtaining the actual measurement data and the prediction dataobtained in step S520 of obtaining the prediction data.

As an example, the compensation value may be calculated by using adescriptive statistical analysis technique. Specifically, thecompensation value may be calculated by comparing a representative valueof the actual measurement data (e.g., a mean value, a median value, amode value, a variance value, and/or a standard deviation value for atleast some of the urine volume measurement values) with a representativevalue of the prediction data (e.g., an average value, a median value, amode value, a variance value, and/or a standard deviation value of atleast some of the urine volume prediction values).

As another example, a compensation value may be calculated by using aregression analysis technique. Specifically, when using a value obtainedfrom the actual measurement data as any one of an independent variableor a dependent variable, and when using a value obtained from theprediction data as the other of the independent variable or thedependent variable, a regression coefficient calculated through theregression analysis technique may be obtained as the compensation value.Here, simple liner regression, multilinear regression, logisticregression, ridge regression, lasso regression, polynomial regression,or non-linear regression may be selected as the regression analysis.

A compensation value may be obtained by using machine learning otherthan multi-dimensional scaling (MDS), principal component analysis(PCA), or regression analysis, in addition to the data analysistechnique described above.

A compensation value may be calculated by using the actual measurementdata group and the prediction data group. In other words, thecompensation value may be calculated by using the actual measurementdata and the prediction data for a single individual, or may becalculated by using the actual measurement data group and predictiondata group for the plurality of individuals, that is, several people. Aspecific method of calculating the compensation value will be describedlater.

In step S540, in the method of correcting the data, target predictiondata may be obtained. The target prediction data may refer to data to becorrected by using the above-described compensation value. For example,the target prediction data may refer to urine volume prediction valuescalculated from the sound data obtained before or obtained newly after adata collection period during which the prediction data or the actualmeasurement data, which are used to calculate the compensation value,are obtained. Naturally, prediction data or urine volume predictionvalues, which are used to obtain the compensation value, may be targetprediction data.

The target prediction data to be corrected may be prediction data for anarbitrary individual. For example, when a prediction data group and anactual measurement data group, which are used to calculate a correctionvalue, include at least of first actual measurement data and firstprediction data for a first individual and second actual measurementdata and second prediction data for a second individual, the target datato be corrected may include the prediction data or the urine volumemeasurement values for the first and second individuals as well as athird individual different from the first and second individuals. Inother words, the aforementioned correction value is not limited to andapplied only to the prediction data of a specific individual involved incalculating the correction value, but may also be used to correct theprediction data of an individual not involved in the calculating of thecorrection value.

In step S550, in the method of correcting the data, the targetprediction data may be corrected by using the correction value. Forexample, a corrected urine volume prediction value may be generated bymultiplying the target urine volume prediction value of the targetprediction data and the correction value.

Hereinafter, the method of correcting the data will be described indetail with reference to FIGS. 24 to 26 .

FIG. 24 is a view illustrating a process of correcting the dataaccording to the exemplary embodiment of the present specification.

FIG. 25 is a graph illustrating a relationship between the actualmeasurement data and the prediction data before data correctionaccording to the exemplary embodiment of the present specification.

FIG. 26 is a graph illustrating a relationship between the actualmeasurement data and the prediction data after the data correctionaccording to the exemplary embodiment of the present specification.

Referring to FIG. 24 , urine volume prediction for a target person maybe performed by using data collected during a data collection period.

In the data collection period, an actual measurement data group may beobtained. The actual measurement data group is data for a plurality ofindividuals, and may refer to a set of actual measurement data for eachindividual. For example, the actual measurement data group may includefirst to s-th actual measurement data for first to s-th individuals.

A sound data group may be obtained in the data collection period. Thesound data group is data for the plurality of individuals, and may referto a set of sound data for each individual. For example, the sound datagroup may include first to t-th sound data for first to t-thindividuals. Sound data for each individual may be understood as sounddata recorded in the urination process during the data collection periodof each individual, and may refer to data obtained by recording soundduring a single urination process or a plurality of urination processes.

A prediction data group may be obtained in the data collection period.The prediction data group is data for the plurality of individuals, andmay refer to a set of prediction data for each individual. For example,the prediction data group may include first to t-th prediction data forfirst to t-th individuals.

The prediction data group may be calculated from the sound data group.For example, first prediction data for a first individual may becalculated from first sound data for the first individual. The firstprediction data may include at least one or more urine volume predictionvalues, and the number of urine volume prediction values included in thefirst prediction data may correspond to the number of urinationprocesses reflected in first sound data.

The first to t-th individuals related to the prediction data group mayinclude the first to s-th individuals related to the actual measurementdata group.

The data collection period may be a short period of time in hours ordays, or a long period of time in months or years. For example, the datacollection period may be 6 hours, 12 hours, 18 hours, 1 day, 4 days, 7days, 10 days, 14 days, 1 month, 3 months, 6 months, or 1 year.

A data set group may be generated from the actual measurement data groupand the prediction data group prior to calculating a correction value.

The data set group may include a plurality of data sets. For example,the data set group may include a data set for each of the plurality ofindividuals.

The data set may be generated from the actual measurement data in theactual measurement data group and the prediction data in the predictiondata group. The data set may include values related to the measurementof urine volumes and values related to the prediction of the urinevolumes. For example, a first data set included in the data set group isgenerated from first actual measurement data and first prediction data,and specifically, may be generated by associating a representativemeasurement value that is a representative value of urine volumeprediction values included in the first actual measurement data and arepresentative prediction value that is a representative value of theurine volume prediction values included in the first prediction data. Inthis case, the representative measurement value may be a sum value, anaverage value, a median value, a mode value, a variance value, or astandard deviation value of the urine volume measurement values includedin the actual measurement data, and the representative prediction valuemay be a sum value, an average value, a median value, a mode value, avariance value, or a standard deviation value of the urine volumeprediction values included in the prediction data.

In other words, the data set may be understood as a concept in which atleast some of the actual measurement data and prediction data for anyone individual are expressed as one set. Accordingly, for oneindividual, one data set may be generated, or a plurality of data setsmay be generated.

As an example, when a data set group is generated from an actualmeasurement data group and a prediction data group, which are collectedfor a plurality of individuals during one day, the data set group mayinclude one data set per individual when a data set includes an averagemeasurement value for one day and an average prediction value for oneday.

As another example, when a data set group is generated from an actualmeasurement data group and a prediction data group, which are collectedfor a plurality of individuals for four days, and four data sets may begenerated per individual when a data set includes an average measurementvalue for one day and an average prediction value for one day.

The process of generating the data set is not limited to theabove-described example. The data set may be generated for each of theplurality of urination processes occurring on the same day, and one dataset may be generated for all urination processes occurring over aplurality of days for each individual.

The data set may be expressed in various forms. For example, as shown inFIGS. 25 and 26 , the data set may be displayed in a form of coordinateson a graph.

A correction value may be calculated from a data set group by using thecorrection process.

As an example, a regression analysis technique may be used incalculating a correction value. Referring to FIG. 25 , a data set groupmay include a data set of which the average of urine volume predictionvalues for each individual is an independent variable and the average ofurine volume measurement values for each individual is a dependentvariable, and thus a function representing a relationship between theurine volume prediction value and the urine volume measurement value maybe obtained by using the data set group. In this case, the obtainedfunction may be a linear function, and a slope thereof may be obtainedas the correction value. In addition, a point at which the urine volumeprediction value has a larger value than the urine volume measurementvalue, the slope at the point may have a value between 0 and 1.

Target prediction data for a target individual may be corrected by usingthe calculated correction value. The target individual may refer to anindividual for which urination information is to be obtained. The targetprediction data may refer to prediction data regarding the urinationprocess of the target individual. For example, a first target predictionvalue may be calculated from sound data obtained by recording sound inthe urination process of a first target individual, and first correcteddata may be obtained by applying the correction value to the firsttarget prediction value. Likewise, a second target prediction value maybe calculated from sound data obtained by recording sound in theurination process of a second target individual, and second correcteddata may be obtained by applying the correction value to the secondtarget prediction value.

The target prediction data may be obtained after the data collectionperiod, and the prediction data obtained during the data collectionperiod may also be the target prediction data.

By using the correction value, the target prediction data may becorrected to be in a range similar to that of the measurement values.Referring to FIGS. 25 and 26 , in FIG. 25 , in data sets of more thanhalf of the data set group before correction, the urine volumeprediction values have larger values than the urine volume measurementvalues so as to be positioned far from a direct proportional referenceline, whereas in FIG. 26 , most data sets in the data set group afterthe correction may be positioned adjacent to the direct proportionalreference line.

In the above, the process of calculating the correction value by usingthe actual measurement data group and the prediction data group for theplurality of individuals has been described, but the technical idea ofthe present specification is not limited thereto, and the predictiondata may also be corrected by using only the actual measurement data forone individual.

As an example, actual measurement data may be collected for a targetindividual during a data collection period, statistical values of thecollected actual measurement data may be calculated, and prediction datafor the target individual may be corrected on the basis of thecalculated statistical values.

Here, the statistical values of the actual measurement data may be atleast one of a mode value, a median value, an average value, a variancevalue, or a standard deviation value of the urine volume measurementvalues measured in the urination process of the target individual duringthe data collection period.

The prediction data for the target individual may be corrected bycorrecting the urine volume prediction values, so as to correspond thestatistical values of the urine volume prediction values included in theprediction data for the target individual to the statistical values ofthe actual measurement data for the target individual.

Meanwhile, the prediction data for the target individual to be thetarget for correction may be data calculated from the sound data on theurination process of the target individual and obtained before the datacollection period, during the data collection period, or after the datacollection period.

Through the method of correcting the data described above, thecorrection of the prediction data for the urination process may beperformed by using the actual measurement data for the urinationprocess, or performed by using the actual measurement data and theprediction data for the urination process. In such a correction process,when the collection of actual measurement data is easy, for example,when measuring a urine volume in the urination process by using a papercup, it may be said that usefulness of the present disclosure issignificantly large in that the correction value is easily calculatedand the corrected prediction data may secure a certain level ofaccuracy.

In the above, features, structures, effects, etc. described in the aboveexemplary embodiments are included in at least one embodiment of thepresent disclosure, and are not necessarily limited to only oneembodiment. Furthermore, the features, structures, effects, etc.illustrated in each embodiment may be implementable by way ofcombinations or modifications for other embodiments by those skilled inthe art to which the embodiments belong. Accordingly, the contentsrelated to such combinations and modifications should be interpreted asbeing included in the scope of the present specification.

In addition, in the above, the present disclosure has been describedfocusing on the embodiments, but these are only examples and do notlimit the technical idea of the present specification, and thus thoseskilled in the art to which this specification pertains will appreciatethat various modifications and applications not exemplified above arepossible without departing from the essential characteristics of thepresent embodiments. That is, each component specifically shown in theembodiments may be implemented by modifications. In addition,differences related to such modifications and applications should beconstrued as being included in the scope of the present specificationdefined in the appended claims.

The invention claimed is:
 1. A method of obtaining high accuracy urination information, comprising: obtaining sound data, wherein the sound data comprises a starting point and an ending point; obtaining first to m-th segmented target data corresponding to m windows from the sound data, wherein each of the m windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, consecutive windows among the m windows partially overlap each other, and m is a natural number greater than or equal to 2; obtaining first to m-th segmented classification data by inputting the first to m-th segmented target data into a pre-trained urination/non-urination classification model, wherein the urination/non-urination classification model is configured to output data comprising at least one value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtaining first to n-th segmented target data corresponding to n windows from the sound data, wherein each of the n windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, consecutive windows among the n windows partially overlap each other, and n is the natural number greater than or equal to 2; obtaining first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into a pre-trained urine flow rate prediction model, wherein the urine flow rate prediction model is configured to output data comprising at least one value for urine flow rate when data related to urination sound are inputted; and obtaining urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data.
 2. The method of claim 1, wherein an overlapping degree of the consecutive windows among the m windows is different from an overlapping degree of consecutive windows among the n windows.
 3. The method of claim 1, wherein an overlapping degree of the consecutive windows among the m windows is less than an overlapping degree of consecutive windows among the n windows.
 4. The method of claim 1, wherein each of the first to m-th segmented target data and the first to n-th segmented target data comprises a feature value extracted from at least a part of the sound data.
 5. The method of claim 1, wherein the obtaining of the first to m-th segmented target data comprises: transforming the sound data to spectrogram data; and obtaining the first to m-th segmented target data corresponding to the first to m-th windows from the spectrogram data.
 6. The method of claim 1, wherein the obtaining the first to m-th segmented target data comprises: obtaining first to m-th segmented sound data corresponding to the first to m-th windows; and transforming each of the first to m-th segmented sound data to spectrogram data to obtain the first to m-th segmented target data.
 7. The method of claim 6, wherein the spectrogram data are obtained by applying a Mel-filter.
 8. The method of claim 1, wherein, the first to m-th segmented classification data are obtained by sequentially inputting the first to m-th segmented target data into the urination/non-urination classification model, and the first to n-th segmented urine flow rate data are obtained by sequentially inputting the first to n-th segmented target data into the urine flow rate prediction model.
 9. The method of claim 1, wherein, the urination/non-urination classification model comprises at least first input layer, first convolution layer, first hidden layer, and first output layer, and the urine flow rate prediction model comprises at least second input layer, second convolution layer, second hidden layer, and second output layer.
 10. The method of claim 1, wherein the obtaining the urination data comprises: obtaining urination classification data using the first to m-th segmented classification data; obtaining candidate urine flow rate data using the first to n-th segmented urine flow rate data; and processing the candidate urine flow rate data using the urination classification data.
 11. The method of claim 10, wherein the urination data are obtained by convolution operating the urination classification data and the candidate urine flow rate data.
 12. The method of claim 1, further comprising: determining whether a urination section exists for the sound data using the first to m-th segmented classification data after obtaining the first to m-th segmented classification data, and wherein, when the urination section exists for the sound data, obtaining the first to n-th segmented target data from the sound data, obtaining the first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into the urine flow rate prediction model, and obtaining the urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data are performed.
 13. A method of obtaining high accuracy urination information, comprising: obtaining sound data by sampling a sound signal obtained by recording a urination process through an external device, wherein the sound data comprises a starting point and ending point; obtaining first to n-th segmented target data corresponding to first to n-th windows from the sound data, wherein each of the first to n-th windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, and n is a natural number greater than or equal to 2; obtaining first to m-th segmented classification data by inputting at least a part of the first to n-th segmented target data into a pre-trained urination/non-urination classification model, wherein the urination/non-urination classification model is configured to output data comprising at least one value for classifying a urination section or a non-urination section when data related to urination sound are inputted, and m is a natural number greater than or equal to 2 and less than or equal to the n; obtaining first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into a pre-trained urine flow rate prediction model, wherein the urine flow rate prediction model is configured to output data including at least one value for urine flow rate when data related to urination sound are inputted; and obtaining urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data.
 14. The method of claim 13, wherein two consecutive windows among the first to n-th windows overlap.
 15. The method of claim 13, wherein two consecutive windows among the first to n-th windows do not overlap each other, and m is equal to n.
 16. The method of claim 13, wherein each of the first to n-th segmented target data comprises a feature value extracted from at least a part of the sound data.
 17. The method of claim 13, wherein the obtaining the first to n-th segmented target data comprises: transforming the sound data to spectrogram data; and obtaining the first to n-th segmented target data corresponding to the first to n-th windows from the spectrogram data.
 18. The method of claim 13, wherein the obtaining the first to n-th segmented target data comprises: obtaining first to n-th segmented sound data corresponding to the first to n-th windows; and transforming each of the first to n-th segmented sound data to spectrogram data to obtain the first to n-th segmented target data.
 19. The method of claim 18, wherein the spectrogram data are Mel-spectrogram data.
 20. The method of claim 13, wherein, the first to m-th segmented classification data are obtained by sequentially inputting m segmented target data corresponding to m windows that do not overlap each other among the first to n-th windows into the urination/non-urination classification model, and the first to n-th segmented urine flow rate data are obtained by sequentially inputting the first to n-th segmented target data into the urine flow rate prediction model.
 21. The method of claim 13, wherein, the urination/non-urination classification model comprises at least first input layer, first convolution layer, first hidden layer, and first output layer, and the urine flow rate prediction model comprises at least second input layer, second convolution layer, second hidden layer, and second output layer, wherein a data form input to the first input layer is same as a data form input to the second input layer.
 22. The method of claim 13, wherein the obtaining of the urination data comprises: obtaining urination classification data using the first to m-th segmented classification data; obtaining candidate urine flow rate data using the first to n-th segmented urine flow rate data; and processing the candidate urine flow rate data using the urination classification data.
 23. The method of claim 22, wherein the urination data are obtained by convolution operating the urination classification data and the candidate urine flow rate data.
 24. A system for obtaining high accuracy urination information, comprising a memory comprising computer readable instructions stored thereon; and a controller comprising a processor configured to execute the computer readable instructions that cause the controller to obtain sound data, wherein the sound data comprises a starting point and an ending point; obtain first to m-th segmented target data corresponding to m windows from the sound data, wherein each of the m windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, consecutive windows among the m windows partially overlap each other, and m is a natural number greater than or equal to 2; obtain first to m-th segmented classification data by inputting the first to m-th segmented target data into a pre-trained urination/non-urination classification model, wherein the urination/non-urination classification model is configured to output data comprising at least one value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtain first to n-th segmented target data corresponding to n windows from the sound data, wherein each of the n windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, consecutive windows among the n windows partially overlap each other, and n is the natural number greater than or equal to 2; obtain first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into a pre-trained urine flow rate prediction model, wherein the urine flow rate prediction model is configured to output data comprising at least one value for urine flow rate when data related to urination sound are inputted; and obtain urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data.
 25. The system of claim 24, wherein the computer readable instructions further cause the controller to: determine whether a urination section exists for the sound data using the first to m-th segmented classification data after the obtaining the first to m-th segmented classification data, wherein when the urination section exists for the sound data, obtain the first to n-th segmented target data from the sound data, obtain the first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into the urine flow rate prediction model, and obtain the urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data are performed.
 26. A non-transitory computer readable storage medium comprising a plurality of computer readable instructions embodied thereon wherein when the computer readable instructions are executed by a controller configure the controller to: obtain high accuracy urination information, executed by the controller of a computerized system comprising a processor and a sound sensor, obtain sound data, wherein the sound data have a starting point and an ending point; obtain first to m-th segmented target data corresponding to m windows from the sound data, wherein each of the m windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, consecutive windows among the m windows partially overlap each other, and m is a natural number greater than or equal to 2; obtain first to m-th segmented classification data by inputting the first to m-th segmented target data into a pre-trained urination/non-urination classification model, wherein the urination/non-urination classification model is configured to output data comprising at least one value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtain first to n-th segmented target data corresponding to n windows from the sound data, wherein each of the n windows comprises a predetermined time period, and is sequentially determined between the starting point and the ending point, consecutive windows among the n windows partially overlap each other, and n is the natural number greater than or equal to 2; obtain first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into a pre-trained urine flow rate prediction model, wherein the urine flow rate prediction model is configured to output data comprising at least one value for urine flow rate when data related to urination sound are inputted; and obtain urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data.
 27. The non-transitory computer readable storage medium of claim 26, wherein when the computer readable instructions are executed by the controller, the computer readable instructions further cause the controller to: determine whether a urination section exists for the sound data using the first to m-th segmented classification data after the obtaining the first to m-th segmented classification data, wherein when the urination section exists for the sound data, obtain the first to n-th segmented target data from the sound data, obtain the first to n-th segmented urine flow rate data by inputting the first to n-th segmented target data into the urine flow rate prediction model, and obtain the urination data using at least the first to m-th segmented classification data and the first to n-th segmented urine flow rate data are performed. 